IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Computationalbiology

Elysium Technologies Private Limited
ISO 9001:2008 A leading Research and Development Division
Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore
Website: elysiumtechnologies.com, elysiumtechnologies.info
Email: info@elysiumtechnologies.com

IEEE Project List 2011 - 2012

Abstract COMPUTATIONAL BIOLOGY AND
BIO INFORMATICS 2011 - 2012

01 3D Shape Reconstruction of Loop Objects in X-Ray Protein Crystallography

Knowledge of the shape of crystals can benefit data collection in X-ray crystallography. A preliminary step is the
determination of the loop object, i.e., the shape of the loop holding the crystal. Based on the standard set-up of
experimental X-ray stations for protein crystallography, the paper reviews a reconstruction method merely requiring 2D
object contours and presents a dedicated novel algorithm. Properties of the object surface (e.g., texture) and depth
information do not have to be considered. The complexity of the reconstruction task is significantly reduced by slicing the
3D object into parallel 2D cross-sections. The shape of each cross-section is determined using support lines forming
polygons. The slicing technique allows the reconstruction of concave surfaces perpendicular to the direction of projection.
In spite of the low computational complexity, the reconstruction method is resilient to noisy object projections caused by
imperfections in the image-processing system extracting the contours. The algorithm developed here has been
successfully applied to the reconstruction of shapes of loop objects in X-ray crystallography.

02 A Biologically Inspired Measure for Co expression Analysis

Two genes are said to be coexpressed if their expression levels have a similar spatial or temporal pattern. Ever since the
profiling of gene microarrays has been in progress, computational modeling of coexpression has acquired a major focus.
As a result, several similarity/distance measures have evolved over time to quantify coexpression similarity/dissimilarity
between gene pairs. Of these, correlation coefficient has been established to be a suitable quantifier of pairwise
coexpression. In general, correlation coefficient is good for symbolizing linear dependence, but not for nonlinear
dependence. In spite of this drawback, it outperforms many other existing measures in modeling the dependency in
biological data. In this paper, for the first time, we point out a significant weakness of the existing similarity/distance
measures, including the standard correlation coefficient, in modeling pairwise coexpression of genes. A novel measure,
called BioSim, which assumes values between 1 and þ1 corresponding to negative and positive dependency and 0 for
independency, is introduced. The computation of BioSim is based on the aggregation of stepwise relative angular deviation
of the expression vectors considered. The proposed measure is analytically suitable for modeling coexpression as it
accounts for the features of expression similarity, expression deviation and also the relative dependence. It is
demonstrated how the proposed measure is better able to capture the degree of coexpression between a pair of genes as
compared to several other existing ones. The efficacy of the measure is statistically analyzed by integrating it with several
module-finding algorithms based on coexpression values and then applying it on synthetic and biological data. The
annotation results of the coexpressed genes as obtained from gene ontology establish the significance of the introduced
measure. By further extending the BioSim measure, it has been shown that one can effectively identify the variability in the
expression patterns over multiple phenotypes. We have also extended BioSim to figure out pairwise differential expression
pattern and coexpression dynamics. The significance of these studies is shown based on the analysis over several real-life
data sets. The computation of the measure by focusing on stepwise time points also makes it effective to identify partially

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Madurai Trichy Kollam
Elysium Technologies Private Limited Elysium Technologies Private Limited Elysium Technologies Private Limited
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eMail: info@elysiumtechnologies.com Contact : 91431 - 4002234. eMail: elysium.kollam@gmail.com
eMail: elysium.tiruchy@gmail.com

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coexpressed genes. On the whole, we put forward a complete framework for coexpression analysis based on the BioSim
measure.

03 A cDNA Microarray Gene Expression Data Classifier for Clinical Diagnostics Based on Graph Theory

Despite great advances in discovering cancer molecular profiles, the proper application of microarray technology to routine
clinical diagnostics is still a challenge. Current practices in the classification of microarrays’ data show two main
limitations: the reliability of the training data sets used to build the classifiers, and the classifiers’ performances, especially
when the sample to be classified does not belong to any of the available classes. In this case, state-of-the-art algorithms
usually produce a high rate of false positives that, in real diagnostic applications, are unacceptable. To address this
problem, this paper presents a new cDNA microarray data classification algorithm based on graph theory and is able to
overcome most of the limitations of known classification methodologies. The classifier works by analyzing gene expression
data organized in an innovative data structure based on graphs, where vertices correspond to genes and edges to gene
expression relationships. To demonstrate the novelty of the proposed approach, the authors present an experimental
performance comparison between the proposed classifier and several state-of-the-art classification algorithms.

04 A Comprehensive Statistical Model for Cell Signaling

Protein signaling networks play a central role in transcriptional regulation and the etiology of many diseases. Statistical
methods, particularly Bayesian networks, have been widely used to model cell signaling, mostly for model organisms and
with focus on uncovering connectivity rather than inferring aberrations. Extensions to mammalian systems have not
yielded compelling results, due likely to greatly increased complexity and limited proteomic measurements in vivo. In this
study, we propose a comprehensive statistical model that is anchored to a predefined core topology, has a limited
complexity due to parameter sharing and uses micorarray data of mRNA transcripts as the only observable components of
signaling. Specifically, we account for cell heterogeneity and a multilevel process, representing signaling as a Bayesian
network at the cell level, modeling measurements as ensemble averages at the tissue level, and incorporating patient-to-
patient differences at the population level. Motivated by the goal of identifying individual protein abnormalities as potential
therapeutical targets, we applied our method to the RAS-RAF network using a breast cancer study with 118 patients. We
demonstrated rigorous statistical inference, established reproducibility through simulations and the ability to recover
receptor status from available microarray data.

05 A Consensus Tree Approach for Reconstructing Human Evolutionary History and Detecting Population Substructure

The random accumulation of variations in the human genome over time implicitly encodes a history of how human
populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a
challenging computational and statistical problem but has important applications both to basic research and to the
discovery of genotypephenotype correlations. We present a novel approach to inferring human evolutionary history from
genetic variation data. We use the idea of consensus trees, a technique generally used to reconcile species trees from
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divergent gene trees, adapting it to the problem of finding robust relationships within a set of intraspecies phylogenies
derived from local regions of the genome. Validation on both simulated and real data shows the method to be effective in
recapitulating known true structure of the data closely matching our best current understanding of human evolutionary
history. Additional comparison with results of leading methods for the problem of population substructure assignment
verifies that our method provides comparable accuracy in identifying meaningful population subgroups in addition to
inferring relationships among them. The consensus tree approach thus provides a promising new model for the robust
inference of substructure and ancestry from large-scale genetic variation data.

06 A Comprehensive Statistical Model for Cell Signaling

The random accumulation of variations in the human genome over time implicitly encodes a history of how human
populations have arisen, dispersed, and intermixed since we emerged as a species. Reconstructing that history is a
challenging computational and statistical problem but has important applications both to basic research and to the
discovery of genotypephenotype correlations. We present a novel approach to inferring human evolutionary history from
genetic variation data. We use the idea of consensus trees, a technique generally used to reconcile species trees from
divergent gene trees, adapting it to the problem of finding robust relationships within a set of intraspecies phylogenies
derived from local regions of the genome. Validation on both simulated and real data shows the method to be effective in
recapitulating known true structure of the data closely matching our best current understanding of human evolutionary
history. Additional comparison with results of leading methods for the problem of population substructure assignment
verifies that our method provides comparable accuracy in identifying meaningful population subgroups in addition to
inferring relationships among them. The consensus tree approach thus provides a promising new model for the robust
inference of substructure and ancestry from large-scale genetic variation data.

07 A Continuous-Time, Discrete-State Method for Simulating the Dynamics of Biochemical Systems

Computational systems biology is largely driven by mathematical modeling and simulation of biochemical networks, via
continuous deterministic methods or discrete event stochastic methods. Although the deterministic methods are efficient
in predicting the macroscopic behavior of a biochemical system, they are severely limited by their inability to represent the
stochastic effects of random molecular fluctuations at lower concentration. In this work, we have presented a novel method
for simulating biochemical networks based on a deterministic solution with a modification that permits the incorporation of
stochastic effects. To demonstrate the feasibility of our approach, we have tested our method on three previously reported
biochemical networks. The results, while staying true to their deterministic form, also reflect the stochastic effects of
random fluctuations that are dominant as the system transitions into a lower concentration. This ability to adapt to a
concentration gradient makes this method particularly attractive for systems biologybased applications.

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08 A Fast Algorithm for Computing Geodesic Distances in Tree Space

Comparing and computing distances between phylogenetic trees are important biological problems, especially for models
where edge lengths play an important role. The geodesic distance measure between two phylogenetic trees with edge
lengths is the length of the shortest path between them in the continuous tree space introduced by Billera, Holmes, and
Vogtmann. This tree space provides a powerful tool for studying and comparing phylogenetic trees, both in exhibiting a
natural distance measure and in providing a euclidean-like structure for solving optimization problems on trees. An
important open problem is to find a polynomial time algorithm for finding geodesics in tree space. This paper gives such an
algorithm, which starts with a simple initial path and moves through a series of successively shorter paths until the
geodesic is attained.

09 A Fast Hierarchical Clustering Algorithm for Functional Modules Discovery in Protein Interaction Networks

As advances in the technologies of predicting protein interactions, huge data sets portrayed as networks have been
available. Identification of functional modules from such networks is crucial for understanding principles of cellular
organization and functions. However, protein interaction data produced by high-throughput experiments are generally
associated with high false positives, which makes it difficult to identify functional modules accurately. In this paper, we
propose a fast hierarchical clustering algorithm HC-PIN based on the local metric of edge clustering value which can be
used both in the unweighted network and in the weighted network. The proposed algorithm HC-PIN is applied to the yeast
protein interaction network, and the identified modules are validated by all the three types of Gene Ontology (GO) Terms:
Biological Process, Molecular Function, and Cellular Component. The experimental results show that HC-PIN is not only
robust to false positives, but also can discover the functional modules with low density. The identified modules are
statistically significant in terms of three types of GO annotations. Moreover, HC-PIN can uncover the hierarchical
organization of functional modules with the variation of its parameter’s value, which is approximatively corresponding to
the hierarchical structure of GO annotations. Compared to other previous competing algorithms, our algorithm HC-PIN is
faster and more accurate.

10 A Framework for Semi supervised Feature Generation and Its Applications in Biomedical Literature Mining

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling
generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features,
i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then

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generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is:
EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the
performance of these low-frequency features can be greatly boosted and new information from unlabeled can be
incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER),
protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features
are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and
TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It
improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree
tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets

11 A General Framework for Analyzing Data from Two Short Time-Series Microarray Experiments

We propose a general theoretical framework for analyzing differentially expressed genes and behavior patterns from two
homogenous short time-course data. The framework generalizes the recently proposed Hilbert-Schmidt Independence
Criterion (HSIC)-based framework [34], [35] adapting it to the time-series scenario by utilizing tensor analysis for data
transformation. The proposed framework is effective in yielding criteria that can identify both the differentially expressed
genes and time-course patterns of interest between two time-series experiments without requiring to explicitly cluster the
data. The results, obtained by applying the proposed framework with a linear kernel formulation, on various data sets are
found to be both biologically meaningful and consistent with published studies.

12 A Genetic Optimization Approach for Isolating Translational Efficiency Bias

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution,
phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most
well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel
approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating
translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially
dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both
genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more
accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this
increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven
particularly difficult for existing approaches due to the presence of strong confounding biases.

13 A Markov-Blanket-Based Model for Gene Regulatory Network Inference

An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale
microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression
data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1)
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discovery of a gene’s Markov Blanket (MB), 2) formulation of a flexible measure to determine the network’s quality, 3)
efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions.
Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic
synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-
degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and
high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its
biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel
interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.

14 A Max-Flow-Based Approach to the Identification of Protein Complexes Using Protein Interaction and Microarray Data

The emergence of high-throughput technologies leads to abundant protein-protein interaction (PPI) data and microarray
gene expression profiles, and provides a great opportunity for the identification of novel protein complexes using
computational methods. By combining these two types of data, we propose a novel Graph Fragmentation Algorithm (GFA)
for protein complex identification. Adapted from a classical max-flow algorithm for finding the (weighted) densest
subgraphs, GFA first finds large (weighted) dense subgraphs in a protein-protein interaction network, and then, breaks
each such subgraph into fragments iteratively by weighting its nodes appropriately in terms of their corresponding log-fold
changes in the microarray data, until the fragment subgraphs are sufficiently small. Our tests on three widely used protein-
protein interaction data sets and comparisons with several latest methods for protein complex identification demonstrate
the strong performance of our method in predicting novel protein complexes in terms of its specificity and efficiency. Given
the high specificity (or precision) that our method has achieved, we conjecture that our prediction results imply more than
200 novel protein complexes.

15 A Note on the Fixed Parameter Tractability of the Gene-Duplication Problem

The NP-hard gene-duplication problem takes as input a collection of gene trees and seeks a species tree that requires the
fewest number of gene duplications to reconcile the input gene trees. An oft-cited, decade-old result by Stege states that
the gene-duplication problem is fixed parameter tractable when parameterized by the number of gene duplications
necessary for the reconciliation. Here, we uncover an error in this fixed parameter algorithm and show that this error cannot
be corrected without sacrificing the fixed parameter tractability of the algorithm. Furthermore, we show a link between the
geneduplication problem and the minimum rooted triplets inconsistency problem which implies that the gene-duplication
problem is 1) W[2]-hard when parameterized by the number of gene duplications necessary for the reconciliation and 2)
hard to approximate to better than a logarithmic factor.

16 A Partial Set Covering Model for Protein Mixture Identification Using Mass Spectrometry Data

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Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are
many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based
methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less
information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification
accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from
MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the
problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that
can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the
combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the
advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based
protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is
further improved.

17 A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

Recently, much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic
trees in order to accommodate complex evolutionary processes. Here, we present an efficient, practical algorithm for
reconstructing level-1 phylogenetic networks—a type of network slightly more general than a phylogenetic tree—from
triplets. Our algorithm has been made publicly available as the program LEV1ATHAN. It combines ideas from several known
theoretical algorithms for phylogenetic tree and network reconstruction with two novel subroutines. Namely, an
exponential-time exact and a greedy algorithm both of which are of independent theoretical interest. Most importantly,
LEV1ATHAN runs in polynomial time and always constructs a level-1 network. If the data are consistent with a phylogenetic
tree, then the algorithm constructs such a tree. Moreover, if the input triplet set is dense and, in addition, is fully consistent
with some level-1 network, it will find such a network. The potential of LEV1ATHAN is explored by means of an extensive
simulation study and a biological data set. One of our conclusions is that LEV1ATHAN is able to construct networks
consistent with a high percentage of input triplets, even when these input triplets are affected by a low to moderate level of
noise.

18 A Spectral Approach to Protein Structure Alignment

A new intrinsic geometry based on a spectral analysis is used to motivate methods for aligning protein folds. The geometry
is induced by the fact that a distance matrix can be scaled so that its eigenvalues are positive. We provide a mathematically
rigorous development of the intrinsic geometry underlying our spectral approach and use it to motivate two alignment
algorithms. The first uses eigenvalues alone and dynamic programming to quickly compute a fold alignment. Family
identification results are reported for the Skolnick40 and Proteus300 data sets. The second algorithm extends our spectral
method by iterating between our intrinsic geometry and the 3D geometry of a fold to make high-quality alignments. Results
and comparisons are reported for several difficult fold alignments. The second algorithm’s ability to correctly identify fold
families in the Skolnick40 and Proteus300 data sets is also established.

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19 A Survey on Methods for Modeling and Analyzing Integrated Biological Networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the
open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have
been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical
formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper
understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how
they operate as an ensemble. With the recent advances in the “omics” technologies, more data is becoming available and,
thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss
methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular
metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization
Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid
Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network
size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and
in particular subsystems of various organisms.

20 A Theoretical Analysis of the Prodrug Delivery System for Treating Antibiotic-Resistant Bacteria

Simulations were carried out to analyze a promising new antimicrobial treatment strategy for targeting antibiotic-resistant
bacteria called the -lactamase-dependent prodrug delivery system. In this system, the antibacterial drugs are delivered as
inactive precursors that only become activated after contact with an enzyme characteristic of many species of antibiotic-
resistant bacteria ( - lactamase enzyme). The addition of an activation step contributes an extra layer of complexity to the
system that can lead to unexpected emergent behavior. In order to optimize for treatment success and minimize the risk of
resistance development, there must be a clear understanding of the system dynamics taking place and how they impact on
the overall response. It makes sense to use a systems biology approach to analyze this method because it can facilitate a
better understanding of the complex emergent dynamics arising from diverse interactions in populations. This paper
contains an initial theoretical examination of the dynamics of this system of activation and an assessment of its therapeutic
potential from a theoretical standpoint using an agent-based modeling approach. It also contains a case study comparison
with real-world results from an experimental study carried out on two prodrug candidate compounds in the literature.

21 A Weighted Principal Component Analysis and Its Application to Gene Expression Data

In this work, we introduce in the first part new developments in Principal Component Analysis (PCA) and in the second part
a new method to select variables (genes in our application). Our focus is on problems where the values taken by each
variable do not all have the same importance and where the data may be contaminated with noise and contain outliers, as is
the case with microarray data. The usual PCA is not appropriate to deal with this kind of problems. In this context, we
propose the use of a new correlation coefficient as an alternative to Pearson’s. This leads to a so-called weighted PCA

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(WPCA). In order to illustrate the features of our WPCA and compare it with the usual PCA, we consider the problem of
analyzing gene expression data sets. In the second part of this work, we propose a new PCA-based algorithm to iteratively
select the most important genes in a microarray data set. We show that this algorithm produces better results when our
WPCA is used instead of the usual PCA. Furthermore, by using Support Vector Machines, we show that it can compete with
the Significance Analysis of Microarrays algorithm

22 Accurate Construction of Consensus Genetic Maps via Integer Linear Programming

We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The
computational problem is to build a consensus map, which is a directed graph that includes and is consistent with all (or,
the vast majority of) the markers in the input maps. However, when markers in the individual maps have ordering conflicts,
the resulting consensus map will contain cycles. Here, we formulate the problem of resolving cycles in the context of a
parsimonious paradigm that takes into account two types of errors that may be present in the input maps, namely, local
reshuffles and global displacements. The resulting combinatorial optimization problem is, in turn, expressed as an integer
linear program. A fast approximation algorithm is proposed, and an additional speedup heuristic is developed. Our
algorithms were implemented in a software tool named MERGEMAP which is freely available for academic use. An
extensive set of experiments shows that MERGEMAP consistently outperforms JOINMAP, which is the most popular tool
currently available for this task, both in terms of accuracy and running time. MERGEMAP is available for download at
http://www.cs.ucr.edu/~yonghui/mgmap.html.

23 Accurate Reconstruction for DNA Sequencing by Hybridization Based on a Constructive Heuristic

Sequencing by hybridization is a promising cost-effective technology for high-throughput DNA sequencing via microarray
chips. However, due to the effects of spectrum errors rooted in experimental conditions, an accurate and fast
reconstruction of original sequences has become a challenging problem. In the last decade, a variety of analyses and
designs have been tried to overcome this problem, where different strategies have different trade-offs in speed and
accuracy. Motivated by the idea that the errors could be identified by analyzing the interrelation of spectrum elements, this
paper presents a constructive heuristic algorithm, featuring an accurate reconstruction guided by a set of well-defined
criteria and rules. Instead of directly reconstructing the original sequence, the new algorithm first builds several accurate
short fragments, which are then carefully assembled into a whole sequence. The experiments on benchmark instance sets
demonstrate that the proposed method can reconstruct long DNA sequences with higher accuracy than current approaches
in the literature.

24 An Approximation Algorithm for the Noah’s Ark Problem with Random Feature Loss

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The phylogenetic diversity (PD) of a set of species is a measure of their evolutionary distinctness based on a phylogenetic
tree. PD is increasingly being adopted as an index of biodiversity in ecological conservation projects. The Noah’s Ark
Problem (NAP) is an NP-Hard optimization problem that abstracts a fundamental conservation challenge in asking to
maximize the expected PD of a set of taxa given a fixed budget, where each taxon is associated with a cost of conservation
and a probability of extinction. Only simplified instances of the problem, where one or more parameters are fixed as
constants, have as of yet been addressed in the literature. Furthermore, it has been argued that PD is not an appropriate
metric for models that allow information to be lost along paths in the tree. We therefore generalize the NAP to incorporate a
proposed model of feature loss according to an exponential distribution and term this problem NAP with Loss (NAPL). In
this paper, we present a pseudopolynomial time approximation scheme for NAPL.

25 An Improved Heuristic Algorithm for Finding Motif Signals in DNA Sequences

The planted ðl; dÞ-motif search problem is a mathematical abstraction of the DNA functional site discovery task. In this
paper, we propose a heuristic algorithm that can find planted ðl; dÞ-signals in a given set of DNA sequences. Evaluations
on simulated data sets demonstrate that the proposed algorithm outperforms current widely used motif finding algorithms.
We also report the results of experiments on real biological data sets..

26 Asymmetric Comparison and Querying of Biological Networks

Comparing and querying the protein-protein interaction (PPI) networks of different organisms is important to infer
knowledge about conservation across species. Known methods that perform these tasks operate symmetrically, i.e., they
do not assign a distinct role to the input PPI networks. However, in most cases, the input networks are indeed
distinguishable on the basis of how the corresponding organism is biologically well characterized. In this paper a new idea
is developed, that is, to exploit differences in the characterization of organisms at hand in order to devise methods for
comparing their PPI networks. We use the PPI network (called Master) of the best characterized organism as a fingerprint to
guide the alignment process to the second input network (called Slave), so that generated results preferably retain the
structural characteristics of the Master network. Technically, this is obtained by generating from the Master a finite
automaton, called alignment model, which is then fed with (a linearization of) the Slave for the purpose of extracting, via the
Viterbi algorithm, matching subgraphs. We propose an approach able to perform global alignment and network querying,
and we apply it on PPI networks. We tested our method showing that the results it returns are biologically relevant.

27 Bayesian Models and Algorithms for Protein Beta-Sheet Prediction

Prediction of the 3D structure greatly benefits from the information related to secondary structure, solvent accessibility,
and nonlocal contacts that stabilize a protein’s structure. We address the problem of Beta-sheet prediction defined as the

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prediction of Beta-strand pairings, interaction types (parallel or antiparallel), and Beta-residue interactions (or contact
maps). We introduce a Bayesian approach for proteins with six or less Beta-strands in which we model the conformational
features in a probabilistic framework by combining the amino acid pairing potentials with a priori knowledge of Beta-strand
organizations. To select the optimum Beta-sheet architecture, we significantly reduce the search space by heuristics that
enforce the amino acid pairs with strong interaction potentials. In addition, we find the optimum pairwise alignment
between Beta-strands using dynamic programming in which we allow any number of gaps in an alignment to model -
bulges more effectively. For proteins with more than six Beta-strands, we first compute Beta-strand pairings using the
BetaPro method. Then, we compute gapped alignments of the paired Beta-strands and choose the interaction types and -
residue pairings with maximum alignment scores. We performed a 10-fold cross-validation experiment on the BetaSheet916
set and obtained significant improvements in the prediction accuracy.

28 Cancer Classification from Gene Expression Data by NPPC Ensemble

The most important application of microarray in gene expression analysis is to classify the unknown tissue samples
according to their gene expression levels with the help of known sample expression levels. In this paper, we present a
nonparallel plane proximal classifier (NPPC) ensemble that ensures high classification accuracy of test samples in a
computer-aided diagnosis (CAD) framework than that of a single NPPC model. For each data set only, a few genes are
selected by using a mutual information criterion. Then a genetic algorithm-based simultaneous feature and model selection
scheme is used to train a number of NPPC expert models in multiple subspaces by maximizing cross-validation accuracy.
The members of the ensemble are selected by the performance of the trained models on a validation set. Besides the usual
majority voting method, we have introduced minimum average proximity-based decision combiner for NPPC ensemble. The
effectiveness of the NPPC ensemble and the proposed new approach of combining decisions for cancer diagnosis are
studied and compared with support vector machine (SVM) classifier in a similar framework. Experimental results on cancer
data sets show that the NPPC ensemble offers comparable testing accuracy to that of SVM ensemble with reduced training
time on average.

29 Comparison of Galled Trees Gabriel

Galled trees, directed acyclic graphs that model evolutionary histories with isolated hybridization events, have become very
popular due to both their biological significance and the existence of polynomial-time algorithms for their reconstruction. In
this paper, we establish to which extent several distance measures for the comparison of evolutionary networks are metrics
for galled trees, and hence, when they can be safely used to evaluate galled tree reconstruction methods.

30 Component-Based Modeling and Reachability Analysis of Genetic Networks

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Genetic regulatory networks usually encompass a multitude of complex, interacting feedback loops. Being able to model
and analyze their behavior is crucial for understanding their function. However, state space explosion is becoming a
limiting factor in the formal analysis of genetic networks. This paper explores a modular approach for verification of
reachability properties. A framework for component-based modeling of genetic regulatory networks, based on a modular
discrete abstraction, is introduced. Then a compositional algorithm to efficiently analyze reachability properties of the
model is proposed. A case study on embryonic cell differentiation involving several hundred cells shows the potential of
this approach.

31 Computing a Smallest Multilabeled Phylogenetic Tree from Rooted Triplets

We investigate the computational complexity of inferring a smallest possible multilabeled phylogenetic tree (MUL tree)
which is consistent with each of the rooted triplets in a given set. This problem has not been studied previously in the
literature. We prove that even the very restricted case of determining if there exists a MUL tree consistent with the input and
having just one leaf duplication is an NP-hard problem. Furthermore, we show that the general minimization problem is
difficult to approximate, although a simple polynomial-time approximation algorithm achieves an approximation ratio close
to our derived inapproximability bound. Finally, we provide an exact algorithm for the problem running in exponential time
and space. As a by-product, we also obtain new, strong inapproximability results for two partitioning problems on directed
graphs called ACYCLIC PARTITION and ACYCLIC TREE-PARTITION.

32 Data Mining on DNA Sequences of Hepatitis B Virus

Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of
the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer)
development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In
this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier
learning, and classification, is introduced. Our research group has collected HBV DNA sequences, either genotype B or C,
from over 200 patients specifically for this project. In the molecular evolution analysis and clustering, three subgroups have
been identified in genotype C and a clustering method has been developed to separate the subgroups. In the feature
selection process, potential markers are selected based on Information Gain for further classifier learning. Then,
meaningful rules are learned by our algorithm called the Rule Learning, which is based on Evolutionary Algorithm. Also, a
new classification method by Nonlinear Integral has been developed. Good performance of this method comes from the use
of the fuzzy measure and the relevant nonlinear integral. The nonadditivity of the fuzzy measure reflects the importance of
the feature attributes as well as their interactions. These two classifiers give explicit information on the importance of the
individual mutated sites and their interactions toward the classification (potential causes of liver cancer in our case). A
thorough comparison study of these two methods with existing methods is detailed. For genotype B, genotype C
subgroups C1, C2, and C3, important mutation markers (sites) have been found, respectively. These two classification
methods have been applied to classify never-seen-before examples for validation. The results show that the classification

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methods have more than 70 percent accuracy and 80 percent sensitivity for most data sets, which are considered high as
an initial scanning method for liver cancer diagnosis.

33 Determination of Glycan Structure from Tandem Mass Spectra

Glycans are molecules made from simple sugars that form complex tree structures. Glycans constitute one of the most
important protein modifications and identification of glycans remains a pressing problem in biology. Unfortunately, the
structure of glycans is hard to predict from the genome sequence of an organism. In this paper, we consider the problem of
deriving the topology of a glycan solely from tandem mass spectrometry (MS) data. We study, how to generate glycan tree
candidates that sufficiently match the sample mass spectrum, avoiding the combinatorial explosion of glycan structures.
Unfortunately, the resulting problem is known to be computationally hard. We present an efficient exact algorithm for this
problem based on fixed-parameter algorithmics that can process a spectrum in a matter of seconds. We also report some
preliminary results of our method on experimental data, combining it with a preliminary candidate evaluation scheme. We
show that our approach is fast in applications, and that we can reach very well de novo identification results. Finally, we
show how to count the number of glycan topologies for a fixed size or a fixed mass. We generalize this result to count the
number of (labeled) trees with bounded out degree, improving on results obtained using Po´ lya’s enumeration theorem.

34 Discriminative Motif Finding for Predicting Protein Subcellular Localization

Many methods have been described to predict the subcellular location of proteins from sequence information. However,
most of these methods either rely on global sequence properties or use a set of known protein targeting motifs to predict
protein localization. Here, we develop and test a novel method that identifies potential targeting motifs using a
discriminative approach based on hidden Markov models (discriminative HMMs). These models search for motifs that are
present in a compartment but absent in other, nearby, compartments by utilizing an hierarchical structure that mimics the
protein sorting mechanism. We show that both discriminative motif finding and the hierarchical structure improve
localization prediction on a benchmark data set of yeast proteins. The motifs identified can be mapped to known targeting
motifs and they are more conserved than the average protein sequence. Using our motif-based predictions, we can identify
potential annotation errors in public databases for the location of some of the proteins. A software implementation and the
data set described in this paper are available from http://murphylab.web.cmu.edu/software/ 2009_TCBB_motif/.

35 Disturbance Analysis of Nonlinear Differential Equation Models of Genetic SUM Regulatory Networks

Noise disturbances and time delays are frequently met in cellular genetic regulatory systems. This paper is concerned with
the disturbance analysis of a class of genetic regulatory networks described by nonlinear differential equation models. The
mechanisms of genetic regulatory networks to amplify (attenuate) external disturbance are explored, and a simple measure
of the amplification (attenuation) level is developed from a nonlinear robust control point of view. It should be noted that the
conditions used to measure the disturbance level are delay-independent or delay-dependent, and are expressed within the
framework of linear matrix inequalities, which can be characterized as convex optimization, and computed by the interior-
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point algorithm easily. Finally, by the proposed method, a numerical example is provided to illustrate how to measure the
attenuation of proteins in the presence of external disturbances.

36 Efficient Formulations for Exact Stochastic Simulation of Chemical Systems

One can generate trajectories to simulate a system of chemical reactions using either Gillespie’s direct method or Gibson
and Bruck’s next reaction method. Because one usually needs many trajectories to understand the dynamics of a system,
performance is important. In this paper, we present new formulations of these methods that improve the computational
complexity of the algorithms. We present optimized implementations, available from http://cain.sourceforge.net/, that offer
better performance than previous work. There is no single method that is best for all problems. Simple formulations often
work best for systems with a small number of reactions, while some sophisticated methods offer the best performance for
large problems and scale well asymptotically. We investigate the performance of each formulation on simple biological
systems using a wide range of problem sizes. We also consider the numerical accuracy of the direct and the next reaction
method. We have found that special precautions must be taken in order to ensure that randomness is not discarded during
the course of a simulation.

37 Encoding Molecular Motions in Voxel Maps

This paper builds on the combination of robotic path planning algorithms and molecular modeling methods for computing
large-amplitude molecular motions, and introduces voxel maps as a computational tool to encode and to represent such
motions. We investigate several applications and show results that illustrate the interest of such representation.

38 Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification

In biomedical data, the imbalanced data problem occurs frequently and causes poor prediction performance for minority
classes. It is because the trained classifiers are mostly derived from the majority class. In this paper, we describe an
ensemble learning method combined with active example selection to resolve the imbalanced data problem. Our method
consists of three key components: 1) an active example selection algorithm to choose informative examples for training the
classifier, 2) an ensemble learning method to combine variations of classifiers derived by active example selection, and 3)
an incremental learning scheme to speed up the iterative training procedure for active example selection. We evaluate the
method on six real-world imbalanced data sets in biomedical domains, showing that the proposed method outperforms
both the random under sampling and the ensemble with under sampling methods. Compared to other approaches to
solving the imbalanced data problem, our method excels by 0.03-0.15 points in AUC measure.

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39 Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network Models on Massively Parallel Computers

We present a novel algorithm to estimate genome-wide gene networks consisting of more than 20,000 genes from gene
expression data using nonparametric Bayesian networks. Due to the difficulty of learning Bayesian network structures,
existing algorithms cannot be applied to more than a few thousand genes. Our algorithm overcomes this limitation by
repeatedly estimating subnetworks in parallel for genes selected by neighbor node sampling. Through numerical
simulation, we confirmed that our algorithm outperformed a heuristic algorithm in a shorter time. We applied our algorithm
to microarray data from human umbilical vein endothelial cells (HUVECs) treated with siRNAs, to construct a human
genome-wide gene network, which we compared to a small gene network estimated for the genes extracted using a
traditional bioinformatics method. The results showed that our genome-wide gene network contains many features of the
small network, as well as others that could not be captured during the small network estimation. The results also revealed
master-regulator genes that are not in the small network but that control many of the genes in the small network. These
analyses were impossible to realize without our proposed algorithm.

40 Estimating Haplotype Frequencies by Combining Data from Large DNA Pools with Database Information

We assume that allele frequency data have been extracted from several large DNA pools, each containing genetic material
of up to hundreds of sampled individuals. Our goal is to estimate the haplotype frequencies among the sampled individuals
by combining the pooled allele frequency data with prior knowledge about the set of possible haplotypes. Such prior
information can be obtained, for example, from a database such as HapMap. We present a Bayesian haplotyping method for
pooled DNA based on a continuous approximation of the multinomial distribution. The proposed method is applicable when
the sizes of the DNA pools and/or the number of considered loci exceed the limits of several earlier methods. In the
example analyses, the proposed model clearly outperforms a deterministic greedy algorithm on real data from the HapMap
database. With a small number of loci, the performance of the proposed method is similar to that of an EM-algorithm, which
uses a multinormal approximation for the pooled allele frequencies, but which does not utilize prior information about the
haplotypes. The method has been implemented using Matlab and the code is available upon request from the authors.

41 EvoMD: An Algorithm for Evolutionary Molecular Design

Traditionally, Computer-Aided Molecular Design (CAMD) uses heuristic search and mathematical programming to tackle the
molecular design problem. But these techniques do not handle large and nonlinear search space very well. To overcome
these drawbacks, graph-based evolutionary algorithms (EAs) have been proposed to evolve molecular design by mimicking
chemical reactions on the exchange of chemical bonds and components between molecules. For these EAs to perform their
tasks, known molecular components, which can serve as building blocks for the molecules to be designed, and known
chemical rules, which govern chemical combination between different components, have to be introduced before the
evolutionary process can take place. To automate molecular design without these constraints, this paper proposes an EA
called Evolutionary Algorithm for Molecular Design (EvoMD). EvoMD encodes molecular designs in graphs. It uses a novel

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crossover operator which does not require known chemistry rules known in advanced and it uses a set of novel mutation
operators. EvoMD uses atomics-based and fragment-based approaches to handle different size of molecule, and the value
of the fitness function it uses is made to depend on the property descriptors of the design encoded in a molecular graph. It
has been tested with different data sets and has been shown to be very promising.

42 Extensions and Improvements to the Chordal Graph Approach to the Multistate Perfect Phylogeny Problem

The multistate perfect phylogeny problem is a classic problem in computational biology. When no perfect phylogeny exists,
it is of interest to find a set of characters to remove in order to obtain a perfect phylogeny in the remaining data. This is
known as the character removal problem. We show how to use chordal graphs and triangulations to solve the character
removal problem for an arbitrary number of states, which was previously unsolved. We outline a preprocessing technique
that speeds up the computation of the minimal separators of a graph. Minimal separators are used in our solution to the
missing data character removal problem and to Gusfield’s solution of the perfect phylogeny problem with missing data.

43 F2Dock: Fast Fourier Protein-Protein Docking

The functions of proteins are often realized through their mutual interactions. Determining a relative transformation for a
pair of proteins and their conformations which form a stable complex, reproducible in nature, is known as docking. It is an
important step in drug design, structure determination, and understanding function and structure relationships. In this
paper, we extend our non uniform fast Fourier transform-based docking algorithm to include an adaptive search phase
(both translational and rotational) and thereby speed up its execution. We have also implemented a multithreaded version
of the adaptive docking algorithm for even faster execution on multi-core machines. We call this protein-protein docking
code F2Dock (F2 ¼ Fast Fourier). We have calibrated F2Dock based on an extensive experimental study on a list of
benchmark complexes and conclude that F2Dock works very well in practice. Though all docking results reported in this
paper use shape complementarity and Coulombic-potential-based scores only, F2Dock is structured to incorporate
Lennard-Jones potential and re ranking docking solutions based on desolvation energy.

44 Fast Surface-Based Travel Depth Estimation Algorithm for Macromolecule Surface Shape Description

Travel Depth, introduced by Coleman and Sharp in 2006, is a physical interpretation of molecular depth, a term frequently
used to describe the shape of a molecular active site or binding site. Travel Depth can be seen as the physical distance a
solvent molecule would have to travel from a point of the surface, i.e., the Solvent-Excluded Surface (SES), to its convex
hull. Existing algorithms providing an estimation of the Travel Depth are based on a regular sampling of the molecule
volume and the use of the Dijkstra’s shortest path algorithm. Since Travel Depth is only defined on the molecular surface,
this volume-based approach is characterized by a large computational complexity due to the processing of unnecessary

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samples lying inside or outside the molecule. In this paper, we propose a surface-based approach that restricts the
processing to data defined on the SES. This algorithm significantly reduces the complexity of Travel Depth estimation and
makes possible the analysis of large macromolecule surface shape description with high resolution. Experimental results
show that compared to existing methods, the proposed algorithm achieves accurate estimations with considerably reduced
processing times.

45 FEAST: Sensitive Local Alignment with Multiple Rates of Evolution

We present a pairwise local aligner, FEAST, which uses two new techniques: a sensitive extension algorithm for identifying
homologous subsequences, and a descriptive probabilistic alignment model. We also present a new procedure for training
alignment parameters and apply it to the human and mouse genomes, producing a better parameter set for these
sequences. Our extension algorithm identifies homologous subsequences by considering all evolutionary histories. It has
higher maximum sensitivity than Viterbi extensions, and better balances specificity. We model alignments with several
submodels, each with unique statistical properties, describing strongly similar and weakly similar regions of homologous
DNA. Training parameters using two submodels produces superior alignments, even when we align with only the
parameters from the weaker submodel. Our extension algorithm combined with our new parameter set achieves sensitivity
0.59 on synthetic tests. In contrast, LASTZ with default settings achieves sensitivity 0.35 with the same false positive rate.
Using the weak submodel as parameters for LASTZ increases its sensitivity to 0.59 with high error. FEAST is available at
http://monod.uwaterloo.ca/feast/.

46 Finding Significant Matches of Position Weight Matrices in Linear Time

Position weight matrices are an important method for modeling signals or motifs in biological sequences, both in DNA and
protein contexts. In this paper, we present fast algorithms for the problem of finding significant matches of such matrices.
Our algorithms are of the online type, and they generalize classical multipattern matching, filtering, and superalphabet
techniques of combinatorial string matching to the problem of weight matrix matching. Several variants of the algorithms
are developed, including multiple matrix extensions that perform the search for several matrices in one scan through the
sequence database. Experimental performance evaluation is provided to compare the new techniques against each other as
well as against some other online and indexbased algorithms proposed in the literature. Compared to the brute-force
OðmnÞ approach, our solutions can be faster by a factor that is proportional to the matrix length m. Our multiple-matrix
filtration algorithm had the best performance in the experiments. On a current PC, this algorithm finds significant matches
(p ¼ 0:0001) of the 123 JASPAR matrices in the human genome in about 18 minutes.

47 Fuzzy ARTMAP Prediction of Biological Activities for Potential HIV-1 Protease Inhibitors Using a Small Molecular Data Set

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Obtaining satisfactory results with neural networks depends on the availability of large data samples. The use of small
training sets generally reduces performance. Most classical Quantitative Structure-Activity Relationship (QSAR) studies for
a specific enzyme system have been performed on small data sets. We focus on the neuro-fuzzy prediction of biological
activities of HIV-1 protease inhibitory compounds when inferring from small training sets. We propose two computational
intelligence prediction techniques which are suitable for small training sets, at the expense of some computational
overhead. Both techniques are based on the FAMR model. The FAMR [1] is a Fuzzy ARTMAP (FAM) incremental learning
system used for classification and probability estimation. During the learning phase, each sample pair is assigned a
relevance factor proportional to the importance of that pair. The two proposed algorithms in this paper are: 1) The GA-
FAMR algorithm, which is new, consists of two stages: a) During the first stage, we use a genetic algorithm (GA) to optimize
the relevances assigned to the training data. This improves the generalization capability of the FAMR. b) In the second
stage, we use the optimized relevances to train the FAMR. 2) The Ordered FAMR is derived from a known algorithm. Instead
of optimizing relevances, it optimizes the order of data presentation using the algorithm of Dagher et al. [2], [3]. In our
experiments, we compare these two algorithms with an algorithm not based on the FAM, the FS-GA-FNN introduced in [4],
[5]. We conclude that when inferring from small training sets, both techniques are efficient, in terms of generalization
capability and execution time. The computational overhead introduced is compensated by better accuracy. Finally, the
proposed techniques are used to predict the biological activities of newly designed potential HIV-1 protease inhibitors.

48 Genetic Networks and Soft Computing

The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes
involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene
expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular
activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as
classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of
networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as
fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable
solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies
and their hybridizations, for the purpose of generating genetic networks.

49 Graph Comparison by Log-Odds Score Matrices with Application to Protein Topology Analysis

A TOPS diagram is a simplified description of the topology of a protein using a graph where nodes are -helices and -
strands, and edges correspond to chirality relations and parallel or antiparallel bonds between strands. We present a
matching algorithm between two TOPS diagrams where the likelihood of a match is measured according to previously
known matches between complete 3D structures. This totally new 3D training is recorded on transition matrices that count
the likelihood that a given TOPS feature, or combination thereof, is replaced by another feature on homologs. The new

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algorithm outperforms existing ones on a benchmark database. Some biologically significant examples are discussed as
well. The method can be used whenever frequencies of edge relationship matches are known, as it is the case for several
biopolymer structures.

50 ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted
Proteins Are Highly Represented

A combination of Integer-Coded Genetic Algorithm (ICGA) and Particle Swarm Optimization (PSO), coupled with the neural-
network-based Extreme Learning Machine (ELM), is used for gene selection and cancer classification. ICGA is used with
PSOELM to select an optimal set of genes, which is then used to build a classifier to develop an algorithm
(ICGA_PSO_ELM) that can handle sparse data and sample imbalance. We evaluate the performance of ICGA-PSO-ELM and
compare our results with existing methods in the literature. An investigation into the functions of the selected genes, using
a systems biology approach, revealed that many of the identified genes are involved in cell signaling and proliferation. An
analysis of these gene sets shows a larger representation of genes that encode secreted proteins than found in randomly
selected gene sets. Secreted proteins constitute a major means by which cells interact with their surroundings. Mounting
biological evidence has identified the tumor microenvironment as a critical factor that determines tumor survival and
growth. Thus, the genes identified by this study that encode secreted proteins might provide important insights to the
nature of the critical biological features in the microenvironment of each tumor type that allow these cells to thrive and
proliferate.

51 Identifiability of Two-Tree Mixtures for Group-Based Models

Phylogenetic data arising on two possibly different tree topologies might be mixed through several biological mechanisms,
including incomplete lineage sorting or horizontal gene transfer in the case of different topologies, or simply different
substitution processes on characters in the case of the same topology. Recent work on a 2-state symmetric model of
character change showed that for 4 taxa, such a mixture model has nonidentifiable parameters, and thus, it is theoretically
impossible to determine the two tree topologies from any amount of data under such circumstances. Here, the question of
identifiability is investigated for two-tree mixtures of the 4-state group-based models, which are more relevant to DNA
sequence data. Using algebraic techniques, we show that the tree parameters are identifiable for the JC and K2P models.
We also prove that generic substitution parameters for the JC mixture models are identifiable, and for the K2P and K3P
models obtain generic identifiability results for mixtures on the same tree. This indicates that the full phylogenetic signal
remains in such mixtures, and the 2-state symmetric result is thus a misleading guide to the behavior of other models.

52 Identification and Modeling of Genes with Diurnal Oscillations from Microarray Time Series Data

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Behavior of living organisms is strongly modulated by the day and night cycle giving rise to a cyclic pattern of activities.
Such a pattern helps the organisms to coordinate their activities and maintain a balance between what could be performed
during the “day” and what could be relegated to the “night.” This cyclic pattern, called the “Circadian Rhythm,” is a
biological phenomenon observed in a large number of organisms. In this paper, our goal is to analyze transcriptome data
from Cyanothece for the purpose of discovering genes whose expressions are rhythmic. We cluster these genes into
groups that are close in terms of their phases and show that genes from a specific metabolic functional category are tightly
clustered, indicating perhaps a “preferred time of the day/ night” when the organism performs this function. The proposed
analysis is applied to two sets of microarray experiments performed under varying incident light patterns. Subsequently,
we propose a model with a network of three phase oscillators together with a central master clock and use it to approximate
a set of “circadian-controlled genes” that can be approximated closely.

53 Identifying Relevant Data for a Biological Database: Handcrafted Rules versus Machine Learning

With well over 1,000 specialized biological databases in use today, the task of automatically identifying novel, relevant data
for such databases is increasingly important. In this paper, we describe practical machine learning approaches for
identifying MEDLINE documents and Swiss-Prot/TrEMBL protein records, for incorporation into a specialized biological
database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by
a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both
the methods compared and the results will be of interest to curators of many specialized databases.

54 Image-Based Surface Matching Algorithm Oriented to Structural Biology

Emerging technologies for structure matching based on surface descriptions have demonstrated their effectiveness in
many research fields. In particular, they can be successfully applied to in silico studies of structural biology. Protein
activities, in fact, are related to the external characteristics of these macromolecules and the ability to match surfaces can
be important to infer information about their possible functions and interactions. In this work, we present a surface-
matching algorithm, based on encoding the outer morphology of proteins in images of local description, which allows us to
establish point-to-point correlations among macromolecular surfaces using image-processing functions. Discarding
methods relying on biological analysis of atomic structures and expensive computational approaches based on energetic
studies, this algorithm can successfully be used for macromolecular recognition by employing local surface features.
Results demonstrate that the proposed algorithm can be employed both to identify surface similarities in context of
macromolecular functional analysis and to screen possible protein interactions to predict pairing capability.

55 Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection

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The gene expression data are usually provided with a large number of genes and a relatively small number of samples,
which brings a lot of new challenges. Selecting those informative genes becomes the main issue in microarray data
analysis. Recursive cluster elimination based on support vector machine (SVM-RCE) has shown the better classification
accuracy on some microarray data sets than recursive feature elimination based on support vector machine (SVM-RFE).
However, SVM-RCE is extremely time-consuming. In this paper, we propose an improved method of SVM-RCE called ISVM-
RCE. ISVM-RCE first trains a SVM model with all clusters, then applies the infinite norm of weight coefficient vector in each
cluster to score the cluster, finally eliminates the gene clusters with the lowest score. In addition, ISVM-RCE eliminates
genes within the clusters instead of removing a cluster of genes when the number of clusters is small. We have tested
ISVM-RCE on six gene expression data sets and compared their performances with SVM-RCE and linear-discriminant-
analysis-based RFE (LDA-RFE). The experiment results on these data sets show that ISVM-RCE greatly reduces the time
cost of SVM-RCE, meanwhile obtains comparable classification performance as SVMRCE, while LDA-RFE is not stable.

56 Incorporating Nonlinear Relationships in Microarray Missing Value Imputation

Microarray gene expression data often contain missing values. Accurate estimation of the missing values is important for downstream
data analyses that require complete data. Nonlinear relationships between gene expression levels have not been wellutilized in
missing value imputation. We propose an imputation scheme based on nonlinear dependencies between genes. By simulations
based on real microarray data, we show that incorporating nonlinear relationships could improve the accuracy of missing value
imputation, both in terms of normalized root-mean-squared error and in terms of the preservation of the list of significant genes in
statistical testing. In addition, we studied the impact of artificial dependencies introduced by data normalization on the simulation
results. Our results suggest that methods relying on global correlation structures may yield overly optimistic simulation results when
the data have been subjected to row (gene)-wise mean removal.

57 Inferring Contagion in Regulatory Networks

Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However,
only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept
defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.e., asymmetries in
gene expression dependences of regulatory networks. Moreover, we present a bootstrap algorithm in order to test the
contagion concept. This technique was applied in simulated data and, also, in an actual large sample of biological data.
Literature review has confirmed some genes identified by contagion as actually belonging to the TP53 pathway.

58 Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks

Constraint-based structure learning algorithms generally perform well on sparse graphs. Although sparsity is not uncommon, there
are some domains where the underlying graph can have some dense regions; one of these domains is gene regulatory networks,
which is the main motivation to undertake the study described in this paper. We propose a new constraint-based algorithm that can
both increase the quality of output and decrease the computational requirements for learning the structure of gene regulatory
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networks. The algorithm is based on and extends the PC algorithm. Two different types of information are derived from the prior
knowledge; one is the probability of existence of edges, and the other is the nodes that seem to be dependent on a large number of
nodes compared to other nodes in the graph. Also a new method based on Gene Ontology for gene regulatory network validation is
proposed. We demonstrate the applicability and effectiveness of the proposed algorithms on both synthetic and real data sets.

59 Information-Theoretic Model of Evolution over Protein Communication Channel

In this paper, we propose a communication model of evolution and investigate its information-theoretic bounds. The
process of evolution is modeled as the retransmission of information over a protein communication channel, where the
transmitted message is the organism’s proteome encoded in the DNA. We compute the capacity and the rate distortion
functions of the protein communication system for the three domains of life: Archaea, Bacteria, and Eukaryotes. The
tradeoff between the transmission rate and the distortion in noisy protein communication channels is analyzed. As
expected, comparison between the optimal transmission rate and the channel capacity indicates that the biological fidelity
does not reach the Shannon optimal distortion. However, the relationship between the channel capacity and rate distortion
achieved for different biological domains provides tremendous insight into the dynamics of the evolutionary processes of
the three domains of life. We rely on these results to provide a model of genome sequence evolution based on the two
major evolutionary driving forces: mutations and unequal crossovers.

60 Learning Genetic Regulatory Network Connectivity from Time Series Data

Recent experimental advances facilitate the collection of time series data that indicate which genes in a cell are expressed. This
information can be used to understand the genetic regulatory network that generates the data. Typically, Bayesian analysis
approaches are applied which neglect the time series nature of the experimental data, have difficulty in determining the direction of
causality, and do not perform well on networks with tight feedback. To address these problems, this paper presents a method to learn
genetic network connectivity which exploits the time series nature of experimental data to achieve better causal predictions. This
method first breaks up the data into bins. Next, it determines an initial set of potential influence vectors for each gene based upon the
probability of the gene’s expression increasing in the next time step. These vectors are then combined to form new vectors with
better scores. Finally, these influence vectors are competed against each other to determine the final influence vector for each gene.
The result is a directed graph representation of the genetic network’s repression and activation connections. Results are reported for
several synthetic networks with tight feedback showing significant improvements in recall and runtime over Yu’s dynamic Bayesian
approach. Promising preliminary results are also reported for an analysis of experimental data for genes involved in the yeast cell
cycle.

61 Linear-Time Algorithms for the Multiple Gene Duplication Problems

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A fundamental problem arising in the evolutionary molecular biology is to discover the locations of gene duplications and
multiple gene duplication episodes based on the phylogenetic information. The solutions to the MULTIPLE GENE
DUPLICATION problems can provide useful clues to place the gene duplication events onto the locations of a species tree
and to expose the multiple gene duplication episodes. In this paper, we study two variations of the MULTIPLE GENE
DUPLICATION problems: the EPISODE-CLUSTERING (EC) problem and the MINIMUM EPISODES (ME) problem. For the EC
problem, we improve the results of Burleigh et al. with an optimal linear-time algorithm. For the ME problem, on the basis of
the algorithm presented by Bansal and Eulenstein, we propose an optimal linear-time algorithm.

62 Manipulating the Steady State of Metabolic Pathways

Metabolic pathways show the complex interactions among enzymes that transform chemical compounds. The state of a
metabolic pathway can be expressed as a vector, which denotes the yield of the compounds or the flux in that pathway at a
given time. The steady state is a state that remains unchanged over time. Altering the state of the metabolism is very
important for many applications such as biomedicine, biofuels, food industry, and cosmetics. The goal of the enzymatic
target identification problem is to identify the set of enzymes whose knockouts lead the metabolism to a state that is close
to a given goal state. Given that the size of the search space is exponential in the number of enzymes, the target
identification problem is very computationally intensive. We develop efficient algorithms to solve the enzymatic target
identification problem in this paper. Unlike existing algorithms, our method works for a broad set of metabolic network
models. We measure the effect of the knockouts of a set of enzymes as a function of the deviation of the steady state of the
pathway after their knockouts from the goal state. We develop two algorithms to find the enzyme set with minimal deviation
from the goal state. The first one is a traversal approach that explores possible solutions in a systematic way using a
branch and bound method. The second one uses genetic algorithms to derive good solutions from a set of alternative
solutions iteratively. Unlike the former one, this one can run for very large pathways. Our experiments show that our
algorithms’ results follow those obtained in vitro in the literature from a number of applications. They also show that the
traversal method is a good approximation of the exhaustive search algorithm and it is up to 11 times faster than the
exhaustive one. This algorithm runs efficiently for pathways with up to 30 enzymes. For large pathways, our genetic
algorithm can find good solutions in less than 10 minutes

63 Metrics on Multilabeled Trees: Interrelationships and Diameter Bounds

Multilabeled trees or MUL-trees, for short, are trees whose leaves are labeled by elements of some nonempty finite set X
such that more than one leaf may be labeled by the same element of X. This class of trees includes phylogenetic trees and
tree shapes. MUL-trees arise naturally in, for example, biogeography and gene evolution studies and also in the area of
phylogenetic network reconstruction. In this paper, we introduce novel metrics which may be used to compare MUL-trees,
most of which generalize well-known metrics on phylogenetic trees and tree shapes. These metrics can be used, for
example, to better understand the space of MUL-trees or to help visualize collections of MUL-trees. In addition, we describe
some relationships between the MUL-tree metrics that we present and also give some novel diameter bounds for these
metrics. We conclude by briefly discussing some open problems as well as pointing out how MUL-tree metrics may be used
to define metrics on the space of phylogenetic networks.

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64 Microarray Time Course Experiments: Finding Profiles

Time course studies with microarray techniques and experimental replicates are very useful in biomedical research. We
present, in replicate experiments, an alternative approach to select and cluster genes according to a new measure for
association between genes. First, the procedure normalizes and standardizes the expression profile of each gene, and then,
identifies scaling parameters that will further minimize the distance between replicates of the same gene. Then, the
procedure filters out genes with a flat profile, detects differences between replicates, and separates genes without
significant differences from the rest. For this last group of genes, we define a mean profile for each gene and use it to
compute the distance between two genes. Next, a hierarchical clustering procedure is proposed, a statistic is computed for
each cluster to determine its compactness, and the total number of classes is determined. For the rest of the genes, those
with significant differences between replicates, the procedure detects where the differences between replicates lie, and
assigns each gene to the best fitting previously identified profile or defines a new profile. We illustrate this new procedure
using simulated data and a representative data set arising from a microarray experiment with replication, and report
interesting results.

65 Model Reduction Using Piecewise-Linear Approximations Preserves Dynamic Properties of the Carbon Starvation Response in
Escherichia coli

The adaptation of the bacterium Escherichia coli to carbon starvation is controlled by a large network of biochemical
reactions involving genes, mRNAs, proteins, and signalling molecules. The dynamics of these networks is difficult to
analyze, notably due to a lack of quantitative information on parameter values. To overcome these limitations, model
reduction approaches based on quasi-steady-state (QSS) and piecewise-linear (PL) approximations have been proposed,
resulting in models that are easier to handle mathematically and computationally. These approximations are not supposed
to affect the capability of the model to account for essential dynamical properties of the system, but the validity of this
assumption has not been systematically tested. In this paper, we carry out such a study by evaluating a large and complex
PL model of the carbon starvation response in E. coli using an ensemble approach. The results show that, in comparison
with conventional nonlinear models, the PL approximations generally preserve the dynamics of the carbon starvation
response network, although with some deviations concerning notably the quantitative precision of the model predictions.
This encourages the application of PL models to the qualitative analysis of bacterial regulatory networks, in situations
where the reference time scale is that of protein synthesis and degradation.

66 Multiclass Kernel-Imbedded Gaussian Processes for Microarray Data Analysis

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Identifying significant differentially expressed genes of a disease can help understand the disease at the genomic level. A
hierarchical statistical model named multiclass kernel-imbedded Gaussian process (mKIGP) is developed under a Bayesian
framework for a multiclass classification problem using microarray gene expression data. Specifically, based on a
multinomial probit regression setting, an empirically adaptive algorithm with a cascading structure is designed to find
appropriate featuring kernels, to discover potentially significant genes, and to make optimal tumor/cancer class
predictions. A Gibbs sampler is adopted as the core of the algorithm to perform Bayesian inferences. A prescreening
procedure is implemented to alleviate the computational complexity. The simulated examples show that mKIGP performed
very close to the Bayesian bound and outperformed the referred state-of-the-art methods in a linear case, a nonlinear case,
and a case with a mislabeled training sample. Its usability has great promises to problems that linear-model-based methods
become unsatisfactory. The mKIGP was also applied to four published real microarray data sets and it was very effective
for identifying significant differentially expressed genes and predicting classes in all of these data sets.

67 Multitask Learning for Protein Subcellular Location Prediction

Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational
methods. The location information can indicate key functionalities of proteins. Thus, accurate prediction of subcellular
localizations of proteins can help the prediction of protein functions and genome annotations, as well as the identification
of drug targets. Machine learning methods such as Support Vector Machines (SVMs) have been used in the past for the
problem of protein subcellular localization, but have been shown to suffer from a lack of annotated training data in each
species under study. To overcome this data sparsity problem, we observe that because some of the organisms may be
related to each other, there may be some commonalities across different organisms that can be discovered and used to
help boost the data in each localization task. In this paper, we formulate protein subcellular localization problem as one of
multitask learning across different organisms. We adapt and compare two specializations of the multitask learning
algorithms on 20 different organisms. Our experimental results show that multitask learning performs much better than the
traditional single-task methods. Among the different multitask learning methods, we found that the multitask kernels and
supertype kernels under multitask learning that share parameters perform slightly better than multitask learning by sharing
latent features. The most significant improvement in terms of localization accuracy is about 25 percent. We find that if the
organisms are very different or are remotely related from a biological point of view, then jointly training the multiple models
cannot lead to significant improvement. However, if they are closely related biologically, the multitask learning can do much
better than individual learning.

68 New Methods for Inference of Local Tree Topologies with Recombinant SNP Sequences in Populations

Large amount of population-scale genetic variation data are being collected in populations. One potentially important
biological problem is to infer the population genealogical history from these genetic variation data. Partly due to
recombination, genealogical history of a set of DNA sequences in a population usually cannot be represented by a single
tree. Instead, genealogy is better represented by a genealogical network, which is a compact representation of a set of
correlated local genealogical trees, each for a short region of genome and possibly with different topology. Inference of
genealogical history for a set of DNA sequences under recombination has many potential applications, including

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association mapping of complex diseases [41], [28], [39]. In this paper, we present two new methods for reconstructing
local tree topologies with the presence of recombination, which extend and improve the previous work in [12], [13], [35]. We
first show that the “tree scan” method [35] can be converted to a probabilistic inference method based on a hidden Markov
model. We then focus on developing a novel local tree inference method called RENT that is both accurate and scalable to
larger data. Through simulation, we demonstrate the usefulness of our methods by showing that the hidden-Markovmodel-
based method is comparable with the original method in [35] in terms of accuracy. We also show that RENT is competitive
with other methods in terms of inference accuracy, and its inference error rate is often lower and can handle large data.

69 Novel Nonlinear Knowledge-Based Mean Force Potentials Based on Machine Learning

The prediction of 3D structures of proteins from amino acid sequences is one of the most challenging problems in
molecular biology. An essential task for solving this problem with coarse-grained models is to deduce effective interaction
potentials. The development and evaluation of new energy functions is critical to accurately modeling the properties of
biological macromolecules. Knowledge-based mean force potentials are derived from statistical analysis of proteins of
known structures. Current knowledgebased potentials are almost in the form of weighted linear sum of interaction pairs. In
this study, a class of novel nonlinear knowledgebased mean force potentials is presented. The potential parameters are
obtained by nonlinear classifiers, instead of relative frequencies of interaction pairs against a reference state or linear
classifiers. The support vector machine is used to derive the potential parameters on data sets that contain both native
structures and decoy structures. Five knowledge-based mean force Boltzmann-based or linear potentials are introduced
and their corresponding nonlinear potentials are implemented. They are the DIH potential (singlebody residue-level
Boltzmann-based potential), the DFIRE-SCM potential (two-body residue-level Boltzmann-based potential), the FS potential
(two-body atom-level Boltzmann-based potential), the HR potential (two-body residue-level linear potential), and the T32S3
potential (two-body atom-level linear potential). Experiments are performed on well-established decoy sets, including the
LKF data set, the CASP7 data set, and the Decoys “R”Us data set. The evaluation metrics include the energy Z score and
the ability of each potential to discriminate native structures from a set of decoy structures. Experimental results show that
all nonlinear potentials significantly outperform the corresponding Boltzmann-based or linear potentials, and the proposed
discriminative framework is effective in developing knowledge-based mean force potentials. The nonlinear potentials can
be widely used for ab initio protein structure prediction, model quality assessment, protein docking, and other challenging
problems in computational biology.

70 On Position-Specific Scoring Matrix for Protein Function Prediction

While genome sequencing projects have generated tremendous amounts of protein sequence data for a vast number of
genomes, substantial portions of most genomes are still unannotated. Despite the success of experimental methods for
identifying protein functions, they are often lab intensive and time consuming. Thus, it is only practical to use in silico
methods for the genomewide functional annotations. In this paper, we propose new features extracted from protein
sequence only and machine learning-based methods for computational function prediction. These features are derived from
a position-specific scoring matrix, which has shown great potential in other bininformatics problems. We evaluate these
features using four different classifiers and yeast protein data. Our experimental results show that features derived from the
position-specific scoring matrix are appropriate for automatic function annotation.

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71 On the Characterization and Selection of Diverse Conformational Ensembles with Applications to Flexible Docking

To address challenging flexible docking problems, a number of docking algorithms pregenerate large collections of
candidate conformers. To remove the redundancy from such ensembles, a central problem in this context is to report a
selection of conformers maximizing some geometric diversity criterion. We make three contributions to this problem. First,
we resort to geometric optimization so as to report selections maximizing the molecular volume or molecular surface area
(MSA) of the selection. Greedy strategies are developed, together with approximation bounds. Second, to assess the
efficacy of our algorithms, we investigate two conformer ensembles corresponding to a flexible loop of four protein
complexes. By focusing on the MSA of the selection, we show that our strategy matches the MSA of standard selection
methods, but resorting to a number of conformers between one and two orders of magnitude smaller. This observation is
qualitatively explained using the Betti numbers of the union of balls of the selection. Finally, we replace the conformer
selection problem in the context of multiple-copy flexible docking. On the aforementioned systems, we show that using the
loops selected by our strategy can improve the result of the docking process.

72 Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices

Pairwise sequence alignment is a central problem in bioinformatics, which forms the basis of various other applications.
Two related sequences are expected to have a high alignment score, but relatedness is usually judged by statistical
significance rather than by alignment score. Recently, it was shown that pairwise statistical significance gives promising
results as an alternative to database statistical significance for getting individual significance estimates of pairwise
alignment scores. The improvement was mainly attributed to making the statistical significance estimation process more
sequence-specific and database-independent. In this paper, we use sequence-specific and position-specific substitution
matrices to derive the estimates of pairwise statistical significance, which is expected to use more sequence-specific
information in estimating pairwise statistical significance. Experiments on a benchmark database with sequence-specific
substitution matrices at different levels of sequence-specific contribution were conducted, and results confirm that using
sequence-specific substitution matrices for estimating pairwise statistical significance is significantly better than using a
standard matrix like BLOSUM62, and than database statistical significance estimates reported by popular database search
programs like BLAST, PSI-BLAST (without pretrained PSSMs), and SSEARCH on a benchmark database, but with pretrained
PSSMs, PSI-BLAST results are significantly better. Further, using position-specific substitution matrices for estimating
pairwise statistical significance gives significantly better results even than PSI-BLAST using pretrained PSSMs.

73 Peak Tree: A New Tool for Multiscale Hierarchical Representation and Peak Detection of Mass Spectrometry Data

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is
greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible
enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we
introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of
scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we
combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition
via both peak intensity and common peak information. The common peak information is derived and loopily refined from

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the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker
selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist
spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits
from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

74 Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms

Progress is continuously being made in the quest for stable biomarkers linked to complex diseases. Mass spectrometers
are one of the devices for tackling this problem. The data profiles they produce are noisy and unstable. In these profiles,
biomarkers are detected as signal regions (peaks), where control and disease samples behave differently. Mass
spectrometry (MS) data generally contain a limited number of samples described by a high number of features. In this work,
we present a novel class of evolutionary algorithms, estimation of distribution algorithms (EDA), as an efficient peak
selector in this MS domain. There is a trade-of f between the reliability of the detected biomarkers and the low number of
samples for analysis. For this reason, we introduce a consensus approach, built upon the classical EDA scheme, that
improves stability and robustness of the final set of relevant peaks. An entire data workflow is designed to yield unbiased
results. Four publicly available MS data sets (two MALDI-TOF and another two SELDI-TOF) are analyzed. The results are
compared to the original works, and a new plot (peak frequential plot) for graphically inspecting the relevant peaks is
introduced. A complete online supplementary page, which can be found at http://www.sc.ehu.es/ccwbayes/members/ruben/
ms, includes extended info and results, in addition to Matlab scripts and references.

75 Predicting Metabolic Fluxes Using Gene Expression Differences As Constraints

A standard approach to estimate intracellular fluxes on a genome-wide scale is flux-balance analysis (FBA), which
optimizes an objective function subject to constraints on (relations between) fluxes. The performance of FBA models
heavily depends on the relevance of the formulated objective function and the completeness of the defined constraints.
Previous studies indicated that FBA predictions can be improved by adding regulatory on/off constraints. These
constraints were imposed based on either absolute [21], [3] or relative [20] gene expression values. We provide a new
algorithm that directly uses regulatory up/down constraints based on gene expression data in FBA optimization (tFBA). Our
assumption is that if the activity of a gene drastically changes from one condition to the other, the flux through the reaction
controlled by that gene will change accordingly. We allow these constraints to be violated, to account for
posttranscriptional control and noise in the data. These up/down constraints are less stringent than the on/off constraints
as previously proposed. Nevertheless, we obtain promising predictions, since many up/down constraints can be enforced.
The potential of the proposed method, tFBA, is demonstrated through the analysis of fluxes in yeast under nine different
cultivation conditions, between which approximately 5,000 regulatory up/down constraints can be defined. We show that
changes in gene expression are predictive for changes in fluxes. Additionally, we illustrate that flux distributions obtained
with tFBA better fit transcriptomics data than previous methods. Finally, we compare tFBA and FBA predictions to show
that our approach yields more biologically relevant results.

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76 Predicting MHC-II Binding Affinity Using Multiple Instance Regression

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is
an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of
MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II
binding peptides is complicated by the significant variability in their length. Most existing computational methods for
predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We
formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance
learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel
method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using
several benchmark data sets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II
binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely
accessible at http://ailab.cs.iastate.edu/mhcmir.

77 Prediction of Protein Functions with Gene Ontology and Interspecies Protein Homology Data

Accurate computational prediction of protein functions increasingly relies on network-inspired models for the protein
function transfer. This task can become challenging for proteins isolated in their own network or those with poor or
uncharacterized neighborhoods. Here, we present a novel probabilistic chain-graph-based approach for predicting protein
functions that builds on connecting networks of two (or more) different species by links of high interspecies sequence
homology. In this way, proteins are able to “exchange” functional information with their neighbors-homologs from a
different species. The knowledge of interspecies relationships, such as the sequence homology, can become crucial in
cases of limited information from other sources of data, including the protein-protein interactions or cellular locations of
proteins. We further enhance our model to account for the Gene Ontology dependencies by linking multiple but related
functional ontology categories within and across multiple species. The resulting networks are of significantly higher
complexity than most traditional protein network models. We comprehensively benchmark our method by applying it to two
largest protein networks, the Yeast and the Fly. The joint Fly-Yeast network provides substantial improvements in
precision, accuracy, and false positive rate over networks that consider either of the sources in isolation. At the same time,
the new model retains the computational efficiency similar to that of the simpler networks.

78 Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays

Probe defects are a major source of noise in gene expression studies. While existing approaches detect noisy probes
based on external information such as genomic alignments, we introduce and validate a targeted probabilistic method for
analyzing probe reliability directly from expression data and independently of the noise source. This provides insights into
the various sources of probe-level noise and gives tools to guide probe design
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79 Recursive Mahalanobis Separability Measure for Gene Subset Selection

Mahalanobis class separability measure provides an effective evaluation of the discriminative power of a feature subset,
and is widely used in feature selection. However, this measure is computationally intensive or even prohibitive when it is
applied to gene expression data. In this study, a recursive approach to Mahalanobis measure evaluation is proposed, with
the goal of reducing computational overhead. Instead of evaluating Mahalanobis measure directly in high-dimensional
space, the recursive approach evaluates the measure through successive evaluations in 2D space. Because of its recursive
nature, this approach is extremely efficient when it is combined with a forward search procedure. In addition, it is noted that
gene subsets selected by Mahalanobis measure tend to overfit training data and generalize unsatisfactorily on unseen test
data, due to small sample size in gene expression problems. To alleviate the overfitting problem, a regularized recursive
Mahalanobis measure is proposed in this study, and guidelines on determination of regularization parameters are provided.
Experimental studies on five gene expression problems show that the regularized recursive Mahalanobis measure
substantially outperforms the nonregularized Mahalanobis measures and the benchmark recursive feature elimination
(RFE) algorithm in all five problems.

80 Regular Networks Can be Uniquely Constructed from Their Trees

A rooted acyclic digraph N with labeled leaves displays a tree T when there exists a way to select a unique parent of each
hybrid vertex resulting in the tree T. Let TrðNÞ denote the set of all trees displayed by the network N. In general, there may
be many other networks M, such that TrðMÞ ¼ TrðNÞ. A network is regular if it is isomorphic with its cover digraph. If N is
regular and D is a collection of trees displayed by N, this paper studies some procedures to try to reconstruct N given D. If
the input is D ¼ TrðNÞ, one procedure is described, which will reconstruct N. Hence, if N and M are regular networks and
TrðNÞ ¼ TrðMÞ, it follows that N ¼ M, proving that a regular network is uniquely determined by its displayed trees. If D is a
(usually very much smaller) collection of displayed trees that satisfies certain hypotheses, modifications of the procedure
will still reconstruct N given D.

81 Robust Feature Selection for Microarray Data Based on Multicriterion Fusion

Mahalanobis class separability measure provides an effective evaluation of the discriminative power of a feature subset,
and is widely used in feature selection. However, this measure is computationally intensive or even prohibitive when it is
applied to gene expression data. In this study, a recursive approach to Mahalanobis measure evaluation is proposed, with
the goal of reducing computational overhead. Instead of evaluating Mahalanobis measure directly in high-dimensional
space, the recursive approach evaluates the measure through successive evaluations in 2D space. Because of its recursive
nature, this approach is extremely efficient when it is combined with a forward search procedure. In addition, it is noted that
gene subsets selected by Mahalanobis measure tend to overfit training data and generalize unsatisfactorily on unseen test
data, due to small sample size in gene expression problems. To alleviate the overfitting problem, a regularized recursive
Mahalanobis measure is proposed in this study, and guidelines on determination of regularization parameters are provided.
Experimental studies on five gene expression problems show that the regularized recursive Mahalanobis measure
substantially outperforms the nonregularized Mahalanobis measures and the benchmark recursive feature elimination
(RFE) algorithm in all five problems.

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82 Searching for Coexpressed Genes in Three-Color cDNA Microarray Data Using a Probabilistic Model-Based Hough Transform

The effects of a drug on the genomic scale can be assessed in a three-color cDNA microarray with the three color
intensities represented through the so-called hexaMplot. In our recent study, we have shown that the Hough Transform (HT)
applied to the hexaMplot can be used to detect groups of coexpressed genes in the normal-disease-drug samples.
However, the standard HT is not well suited for the purpose because 1) the assayed genes need first to be hard-partitioned
into equally and differentially expressed genes, with HT ignoring possible information in the former group; 2) the hexaMplot
coordinates are negatively correlated and there is no direct way of expressing this in the standard HT and 3) it is not clear
how to quantify the association of coexpressed genes with the line along which they cluster. We address these deficiencies
by formulating a dedicated probabilistic model-based HT. The approach is demonstrated by assessing effects of the drug
Rg1 on homocysteine-treated human umbilical vein endothetial cells. Compared with our previous study, we robustly
detect stronger natural groupings of coexpressed genes. Moreover, the gene groups show coherent biological functions
with high significance, as detected by the Gene Ontology analysis.

83 Semantics and Ambiguity of Stochastic RNA Family Models

Stochastic models, such as hidden Markov models or stochastic context-free grammars (SCFGs) can fail to return the
correct, maximum likelihood solution in the case of semantic ambiguity. This problem arises when the algorithm
implementing the model inspects the same solution in different guises. It is a difficult problem in the sense that proving
semantic nonambiguity has been shown to be algorithmically undecidable, while compensating for it (by coalescing scores
of equivalent solutions) has been shown to be NP-hard. For stochastic context-free grammars modeling RNA secondary
structure, it has been shown that the distortion of results can be quite severe. Much less is known about the case when
stochastic context-free grammars model the matching of a query sequence to an implicit consensus structure for an RNA
family. We find that three different, meaningful semantics can be associated with the matching of a query against the
model—a structural, an alignment, and a trace semantics. Rfam models correctly implement the alignment semantics, and
are ambiguous with respect to the other two semantics, which are more abstract. We show how provably correct models
can be generated for the trace semantics. For approaches, where such a proof is not possible, we present an automated
pipeline to check post factum for ambiguity of the generated models. We propose that both the structure and the trace
semantics are worth-while concepts for further study, possibly better suited to capture remotely related family members.

84 Semi-Markov Models for Brownian Dynamics Permeation in Biological Ion Channels

Constructing accurate computational models that explain how ions permeate through a biological ion channel is an
important problem in biophysics and drug design. Brownian dynamics simulations are large-scale interacting particle
computer simulations for modeling ion channel permeation but can be computationally prohibitive. In this paper, we show
the somewhat surprising result that a small-dimensional semi-Markov model can generate events (such as conduction
events and dwell times at binding sites in the protein) that are statistically indistinguishable from Brownian dynamics
computer simulation. This approach enables the use of extrapolation techniques to predict channel conduction when

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performing the actual Brownian dynamics simulation that is computationally intractable. Numerical studies on the
simulation of gramicidin A ion channels are presented

85 Simultaneous Identification of Duplications and Lateral Gene Transfers

The incongruency between a gene tree and a corresponding species tree can be attributed to evolutionary events such as gene
duplication and gene loss. This paper describes a combinatorial model where so-called DTL-scenarios are used to explain the
differences between a gene tree and a corresponding species tree taking into account gene duplications, gene losses, and lateral
gene transfers (also known as horizontal gene transfers). The reasonable biological constraint that a lateral gene transfer may only
occur between contemporary species leads to the notion of acyclic DTL-scenarios. Parsimony methods are introduced by defining
appropriate optimization problems. We show that finding most parsimonious acyclic DTL-scenarios is NP-hard. However, by dropping
the condition of acyclicity, the problem becomes tractable, and we provide a dynamic programming algorithm as well as a
fixedparameter tractable algorithm for finding most parsimonious DTL-scenarios.

86 TCLUST: A Fast Method for Clustering Genome-Scale Expression Data

Genes with a common function are often hypothesized to have correlated expression levels in mRNA expression data,
motivating the development of clustering algorithms for gene expression data sets. We observe that existing approaches
do not scale well for large data sets, and indeed did not converge for the data set considered here. We present a novel
clustering method TCLUST that exploits coconnectedness to efficiently cluster large, sparse expression data. We compare
our approach with two existing clustering methods CAST and K-means which have been previously applied to clustering of
gene-expression data with good performance results. Using a number of metrics, TCLUST is shown to be superior to or at
least competitive with the other methods, while being much faster. We have applied this clustering algorithm to a genome-
scale gene-expression data set and used gene set enrichment analysis to discover highly significant biological clusters.
(Source code for TCLUST is downloadable at http:// www.cse.ucsd.edu/~bdost/tclust.)

87 The Impact of Multiple Protein Sequence Alignment on Phylogenetic Estimation

Multiple sequence alignment is typically the first step in estimating phylogenetic trees, with the assumption being that as
alignments improve, so will phylogenetic reconstructions. Over the last decade or so, new multiple sequence alignment
methods have been developed to improve comparative analyses of protein structure, but these new methods have not been
typically used in phylogenetic analyses. In this paper, we report on a simulation study that we performed to evaluate the
consequences of using these new multiple sequence alignment methods in terms of the resultant phylogenetic
reconstruction. We find that while alignment accuracy is positively correlated with phylogenetic accuracy, the amount of
improvement in phylogenetic estimation that results from an improved alignment can range from quite small to substantial.
We observe that phylogenetic accuracy is most highly correlated with alignment accuracy when sequences are most
difficult to align, and that variation in alignment accuracy can have little impact on phylogenetic accuracy when alignment
error rates are generally low. We discuss these observations and implications for future work.

88 The Plexus Model for the Inference of Ancestral Multidomain Proteins

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Interactions of protein domains control essential cellular processes. Thus, inferring the evolutionary histories of
multidomain proteins in the context of their families can provide rewarding insights into protein function. However,
methods to infer these histories are challenged by the complexity of macroevolutionary events. Here, we address this
challenge by describing an algorithm that computes a novel network-like structure, called plexus, which represents the
evolution of domains and their combinations. Finally, we demonstrate the performance of this algorithm with empirical data
sets.

89 Topology Improves Phylogenetic Motif Functional Site Predictions

Prediction of protein functional sites from sequence-derived data remains an open bioinformatics problem. We have
developed a phylogenetic motif (PM) functional site prediction approach that identifies functional sites from alignment
fragments that parallel the evolutionary patterns of the family. In our approach, PMs are identified by comparing tree
topologies of each alignment fragment to that of the complete phylogeny. Herein, we bypass the phylogenetic
reconstruction step and identify PMs directly from distance matrix comparisons. In order to optimize the new algorithm, we
consider three different distance matrices and 13 different matrix similarity scores. We assess the performance of the
various approaches on a structurally nonredundant data set that includes three types of functional site definitions. Without
exception, the predictive power of the original approach outperforms the distance matrix variants. While the distance matrix
methods fail to improve upon the original approach, our results are important because they clearly demonstrate that the
improved predictive power is based on the topological comparisons. Meaning that phylogenetic trees are a straightforward,
yet powerful way to improve functional site prediction accuracy. While complementary studies have shown that topology
improves predictions of protein-protein interactions, this report represents the first demonstration that trees improve
functional site predictions as well.

90 Toward a Robust Search Method for the Protein-Drug Docking Problem

Predicting the binding mode(s) of a drug molecule to a target receptor is pivotal in structure-based rational drug design. In
contrast to most approaches to solve this problem, the idea in this paper is to analyze the search problem from a
computational perspective. By building on top of an existing docking tool, new methods are proposed and relevant
computational results are proven. These methods and results are applicable for other place-and-join frameworks as well. A
fast approximation scheme for the docking of rigid fragments is described that guarantees certain geometric approximation
factors. It is also demonstrated that this can be translated into an energy approximation for simple scoring functions. A
polynomial time algorithm is developed for the matching phase of the docked rigid fragments. It is demonstrated that the
generic matching problem is NP-hard. At the same time, the optimality of the proposed algorithm is proven under certain
scoring function conditions. The matching results are also applicable for some of the fragment-based de novo design
methods. On the practical side, the proposed method is tested on 829 complexes from the PDB. The results show that the
closest predicted pose to the native structure has the average RMS deviation of 1.06 A bar.

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91 Toward Better Understanding of Protein Secondary Structure: Extracting Prediction Rules

Although numerous computational techniques have been applied to predict protein secondary structure (PSS), only limited
studies have dealt with discovery of logic rules underlying the prediction itself. Such rules offer interesting links between
the prediction model and the underlying biology. In addition, they enhance interpretability of PSS prediction by providing a
degree of transparency to the predicting model usually regarded as a black box. In this paper, we explore the generation
and use of C4.5 decision trees to extract relevant rules from PSS predictions modeled with two-stage support vector
machines (TS-SVM). The proposed rules were derived on the RS126 data set of 126 nonhomologous globular proteins and
on the PSIPRED data set of 1,923 protein sequences. Our approach has produced sets of comprehensible, and often
interpretable, rules underlying the PSS predictions. Moreover, many of the rules seem to be strongly supported by
biological evidence. Further, our approach resulted in good prediction accuracy, few and usually compact rules, and rules
that are generally of higher confidence levels than those generated by other rule extraction techniques.

92 TRIAL: A Tool for Finding Distant Structural Similarities

Finding structural similarities in distantly related proteins can reveal functional relationships that can not be identified
using sequence comparison. Given two proteins A and B and threshold A, we develop an algorithm, TRiplet-based
Iterative ALignment (TRIAL) for computing the transformation of B that maximizes the number of aligned residues such that
the root mean square deviation (RMSD) of the alignment is at most A. Our algorithm is designed with the specific goal
of effectively handling proteins with low similarity in primary structure, where existing algorithms perform particularly
poorly. Experiments show that our method outperforms existing methods. TRIAL alignment brings the secondary
structures of distantly related proteins to similar orientations. It also finds larger number of secondary structure matches at
lower RMSD values and increased overall alignment lengths. Its classification accuracy is up to 63 percent better than other
methods, including CE and DALI. TRIAL successfully aligns 83 percent of the residues from the smaller protein in
reasonable time while other methods align only 29 to 65 percent of the residues for the same set of proteins.

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93 True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction

Gene function prediction is a complex computational problem, characterized by several items: the number of functional
classes is large, and a gene may belong to multiple classes; functional classes are structured according to a hierarchy;
classes are usually unbalanced, with more negative than positive examples; class labels can be uncertain and the
annotations largely incomplete; to improve the predictions, multiple sources of data need to be properly integrated. In this
contribution, we focus on the first three items, and, in particular, on the development of a new method for the hierarchical
genome-wide and ontology-wide gene function prediction. The proposed algorithm is inspired by the “true path rule” (TPR)
that governs both the Gene Ontology and FunCat taxonomies. According to this rule, the proposed TPR ensemble method
is characterized by a two-way asymmetric flow of information that traverses the graph-structured ensemble: positive
predictions for a node influence in a recursive way its ancestors, while negative predictions influence its offsprings. Cross-
validated results with the model organism S. Crevisiae, using seven different sources of biomolecular data, and a
theoretical analysis of the the TPR algorithm show the effectiveness and the drawbacks of the proposed

94 Two-Step Cross-Entropy Feature Selection for Microarrays—Power Through Complementarity

Current feature selection methods for supervised classification of tissue samples from microarray data generally fail to
exploit complementary discriminatory power that can be found in sets of features [10]. Using a feature selection method
with the computational architecture of the cross-entropy method [16], including an additional preliminary step ensuring a
lower bound on the number of times any feature is considered, we show when testing on a human lymph node data set that
there are a significant number of genes that perform well when their complementary power is assessed, but “pass under
the radar” of popular feature selection methods that only assess genes individually on a given classification tool. We also
show that this phenomenon becomes more apparent as diagnostic specificity of the tissue samples analysed increases.

95 Uncovering Hidden Phylogenetic Consensus in Large Data Sets

Many of the steps in phylogenetic reconstruction can be confounded by “rogue” taxa—taxa that cannot be placed with
assurance anywhere within the tree, indeed, whose location within the tree varies with almost any choice of algorithm or
parameters. Phylogenetic consensus methods, in particular, are known to suffer from this problem. In this paper, we
provide a novel framework to define and identify rogue taxa. In this framework, we formulate a bicriterion optimization
problem, the relative information criterion, that models the net increase in useful information present in the consensus tree
when certain taxa are removed from the input data. We also provide an effective greedy heuristic to identify a subset of
rogue taxa and use this heuristic in a series of experiments, with both pathological examples from the literature and a
collection of large biological data sets. As the presence of rogue taxa in a set of bootstrap replicates can lead to deceivingly
poor support values, we propose a procedure to recompute support values in light of the rogue taxa identified by our
algorithm; applying this procedure to our biological data sets caused a large number of edges to move from “unsupported”
to “supported” status, indicating that many existing phylogenies should be recomputed and reevaluated to reduce any
inaccuracies introduced by rogue taxa. We also discuss the implementation issues encountered while integrating our
algorithm into RAxML v7.2.7, particularly those dealing with scaling up the analyses. This integration enables practitioners

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to benefit from our algorithm in the analysis of very large data sets (up to 2,500 taxa and 10,000 trees, although we present
the results of even larger analyses).

96 Using Qualitative Probability in Reverse-Engineering Gene Regulatory Networks

This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in
the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a
study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner
that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a
model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a
set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning
algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets
of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is
compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.

97 Visual Exploration across Biomedical Databases

Though biomedical research often draws on knowledge from a wide variety of fields, few visualization methods for
biomedical data incorporate meaningful cross-database exploration. A new approach is offered for visualizing and
exploring a querybased subset of multiple heterogeneous biomedical databases. Databases are modeled as an entity-
relation graph containing nodes (database records) and links (relationships between records). Users specify a keyword
search string to retrieve an initial set of nodes, and then explore intra- and interdatabase links. Results are visualized with
user-defined semantic substrates to take advantage of the rich set of attributes usually present in biomedical data.
Comments from domain experts indicate that this visualization method is potentially advantageous for biomedical
knowledge exploration.

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IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Computationalbiology

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IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Computationalbiology