Application of Microarray Technology and softcomputing in cancer Biology

P.K.Vaishali & Dr. A.vinayababu
International Journal of Biometrics and Bioinformatics (IJBB), Volume (5) : Issue (4) : 2011 225
Application of Microarray Technology and Softcomputing in
Cancer Biology : A Review
P.K.Vaishali vaishali5599@gmail.com
Department of Computer Science & Information Technology,
Jyothishmathi Institute of Technology & Science,
JNTU, Hyderabad, AP, INDIA.
Dr.A.Vinayababu dravinayababu@yahoo.com
Professor of CSE, Director of Admissions
JNTUH University,
Hyderabad, AP, INDIA.
Abstract
DNA microarray technology has emerged as a boon to the scientific community in understanding the
growth and development of life as well as in widening their knowledge in exploring the genetic
causes of anomalies occurring in the working of the human body. microarray technology makes
biologists be capable of monitoring expression of thousands of genes in a single experiment on a
small chip. Extracting useful knowledge and info from these microarray has attracted the attention of
many biologists and computer scientists. Knowledge engineering has revolutionalized the way in
which the medical data is being looked at. Soft computing is a branch of computer science capable of
analyzing complex medical data. Advances in the area of microarray –based expression analysis
have led to the promise of cancer diagnosis using new molecular based approaches. Many studies
and methodologies have come up which analyszes the gene espression data by using the
techniques in data mining such as feature selection, classification, clustering etc. emboiding the soft
computing methods for more accuracy. This review is an attempt to look at the recent advances in
cancer research with DNA microarray technology , data mining and soft computing techniques.
Keywords: DNA Microarray, Classification, Data Mining ,Soft Computing ,Gene Expression.
1. INTRODUCTION
Deoxyribonucleic acid (DNA) micro array technology provides tools for studying the expression levels
of a large number of distinct genes simultaneously [9]. Micro array technology allows biologists to
simultaneously measure the expressions of thousands of genes in a single experiment [8] [10] [11].
Gene expression data is widely used in disease analysis and cancer diagnosis [5]. Gene expression
data from DNA micro arrays are characterized by many measured variables (genes) on only a few
observations (experiments) although both the number of experiments and genes per experiment are
growing rapidly [4] [6]. Gene expression data from DNA micro array can be characterized by many
variables (genes), but with only a few observations (experiments). Prediction, classification, and
clustering techniques are being used for analysis and interpretation of the data [1]. An important
application of gene expression micro array data is classification of biological samples or prediction of
clinical and other outcomes [2]. Micro array technology is to classify the tissue samples using their
gene expression profiles as one of the several types (or subtypes) of cancer. Compared with the
standard histopathological tests, the gene expression profiles measured through micro array
technology provide accurate, reliable and objective cancer classification. The DNA micro array data
for cancer classification consists of large number of genes (dimensions) compared to the number of
samples or feature vectors [3] [7]. Classification analysis of micro array gene expression data has
been widely used to uncover biological features and to distinguish closely related cell types that often
appear in the diagnosis of cancer [37].Many researchers have developed and demonstrated different
classification techniques for cancer classification based on micro array gene expression data. Feature
selection techniques [12],[13] have been suggested before classification, which finds the top features
that discriminate various classes. Kernel based techniques [14],[15] like SVM have already been used

for binary disease classification problems. Gene selection[16] and neural networks[17] based
classifications were also reported in microarray data analysis. soft computing has been successively
used in bioinformatics thereby providing low cost, low ,better approximation and indeed good and
more accurate solutions.
2. DNA MICROARRAY TECHNOLOGY
Although all of the cells in the human body contain the same genetic material, the same genes are not
active in all of those cells. Studying which genes are active and which are inactive in different kinds of
cells helps scientists understand more about how these cells function and about what happens when
the genes in a cell don’t function properly. In the past scientists have only been able to conduct such
genetic analyses on a few genes at once. With the development of DNA microarray technology,
however, scientists can now examine thousands of genes at the same time, an advance that will help
them determine the complex relationships between individual genes. The mountain of information that
is the draft sequence of the human genome may be impressive, but without interpretation that is all it
remains — a mass of data. Gene function is one of the key elements researchers want to extract from
the sequence, and the DNA microarray is one of the most important tools at their disposal.Microarray
technology will help researchers learn more about many different diseases—heart disease, mental
illness, and infectious disease, to name only a few. One intense area of microarray research at the
NIH is the study of cancer.In the past, scientists have classified different types of cancer based on the
organs in which the tumors develop. With the help of microarray technology, however, they will be
able to further classify these types of cancer based on the patterns of gene activity in the tumor cells
and will then be able to design treatment strategies targeted directly to each specific type of cancer.
Additionally, by examining the differences in gene activity between untreated and treated—radiated or
oxygen-starved, for example—tumor cells, scientists can better understand how different types of
cancer therapies affect tumors and can develop more effective treatments.
In short the usefulness of dna technology can be listed as
1.Can follow the activity of MANY genes at the same time.
2.Can get a lot of results fast
3.Can COMPARE the activity of many genes in diseased and healthy cells
4.Can categorize diseases into subgroups
Microarray Technology: Application in medical
Gene discovery: DNA Microarray technology helps in the identification of new genes, know about
their functioning and expression levels under different conditions.
Disease Diagnosis: DNA Microarray technology helps researchers learn more about different
diseases such as heart diseases, mental illness, infectious disease and especially the study of
cancer. Until recently, different types of cancer have been classified on thedevelop. Now, with the
evolution of microarray technology, it will be possible for the researchers to further classify the types
of cancer on the basis of the patterns of gene activity in the tumor cells. This will tremendously help
the pharmaceutical community to develop more effective drugs as the treatment strategies will be
targeted directly to the specific type of cancer.
Drug Discovery: Microarray technology has extensive application in Pharmacogenomics.
Pharmacogenomics is the study of correlations between therapeutic responses to drugs and the
genetic profiles of the patients. Comparative analysis of the genes from a diseased and a normal cell
will help the identification of the biochemical constitution of the proteins synthesized by the diseased
genes. The researchers can use this information to synthesize drugs which combat with these
proteins and reduce their effect.
Toxicological Research: Microarray technology provides a robust platform for the research of the
impact of toxins on the cells and their passing on to the progeny. Toxicogenomics establishes
correlation between responses to toxicants and the changes in the genetic profiles of the cells
exposed to such toxicants

3. DATAMINING AND SOFT COMPUTING PARADIGM IN THE AREA OF GENE
EXPRESSION IN CANCER DATA - RECENT RELATED RESEARCH : A REVIEW
There exists considerable literature on the application of different soft computing paradigm in the area
of gene expression cancer data sets:One of the first landmark studies using microarray data to
analyze tumor samples was done by Golub et al. [18] . This study on human acute leukemia showed
that it was possible to use microarray data to distinguish between acute myeloid leukemia (AML) and
acute lymphoblastic leukemia (ALL) without any previous knowledge. For the first time the potential of
microarray data was shown by illustrating its use in discovering new classes using the previously
introduced class discovery methods (i.e., unsupervised analysis) and, second, by using microarray
data to assign tumors to known classes (i.e., supervised analysis).
Perou et al. [19] did a similar analysis using hierarchical clustering on breast cancer and found
different groups of breast tumors.
Alon et al. [20] adopted a two-way clustering method whereby both genes and tumors were clustered.
They showed that colon tumors and normal colon tissues were separated based on the microarray
data. Also, they showed that co-regulated families of genes also clustered together.
Tibshirani et al. [21] built further on the development of supervised microarray data analysis methods
by developing the nearest shrunken centroid method, also known as PAM. This technique not only
allows predicting of classes, but also tries to limit the number of genes necessary to make the
prediction. By limiting the number of genes, it is possible to develop cheaper methods to make a
diagnostic test, such as smaller microarrays or quantitative PCR. After these studies research groups
focused more on class.
Ahmad M. Sarhan [22] has presented a stomach cancer detection system based on Artificial Neural
Network (ANN), and the Discrete Cosine Transform (DCT). The proposed system has extracted
classification features from stomach micro arrays using the DCT. The features extracted from the
DCT coefficients have been then applied to an ANN for classification (tumor or non-tumor). The micro
array images used in this study have been obtained from the Stanford Medical Database (SMD).
Simulation results have shown that the proposed system produces a very high success rate.
Bharathi et al. [23] have attempted to find the smallest set of genes that can ensure highly accurate
classification of cancer from micro array data by using supervised machine learning algorithms. The
significance of finding the minimum gene subset has been three fold:1) It has greatly reduced the
computational burden and noise arising from irrelevant genes.2) It has simplified gene expression
tests to include only a very small number of genes rather than thousands of genes, which could
significantly bring down the cost for cancer testing. 3) It has called for further investigation into the
possible biological relationship between these small numbers of genes and cancer development and
treatment. Their simple yet very effective method has involved two steps. In the first step, they have
chosen some important genes using a 2 way Analysis of Variance (ANOVA) ranking scheme. In the
second step, they have tested the classification capability of all simple combinations of those
important genes using a good classifier such as Support Vector Machines. Their approach has
obtained very high accuracy with only two genes.
Bo Li et al. [24] have discussed that the gene expression data collected from DNA micro array are
characterized by a large amount of variables (genes), but with only a small amount of observations
(experiments). They have proposed a manifold learning method to map the gene expression data to a
low dimensional space, and then explore the intrinsic structure of the features so as to classify the
micro array data more accurately. The proposed algorithm could project the gene expression data into
a subspace with high intra-class compactness and inter-class separability. Experimental results on six
DNA micro array datasets have demonstrated that their method is efficient for discriminant feature
extraction and gene expression data classification. Their work is a meaningful attempt to analyze
micro array data using manifold learning method; there should be much room for the application of
manifold learning to bioinformatics due to its performance.

Xiaosheng Wang et al. [25] have discussed that a gene selection is of vital importance in molecular
classification of cancer using high-dimensional gene expression data. Because of the distinct
characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust
feature selection methods has been extremely crucial. They have investigated the properties of one
feature selection approach proposed in their previous work, which has been the generalization of the
feature selection method based on the depended degree of attribute in rough sets. They have
compared the feature selection method with the established methods with respect to the depended
degree, chi-square, information gain, Relief-F and symmetric uncertainty, and analyzed its properties
through a series of classification experiments. The results have revealed that their method is superior
to the canonical depended degree of attribute based method in robustness and applicability.
Moreover, their method has been comparable with the other four commonly used methods. More
importantly, their method could exhibit the inherent classification difficulty with respect to different
gene expression datasets, indicating the inherent biology of specific cancers.
Mallika et al. [26] have presented a novel method for improving classification performance for cancer
classification with very few micro array Gene expression data. The method employs classification with
individual gene ranking and gene subset ranking. For selection and classification, the proposed
method has used the same classifier. The method has been applied to three publicly available cancer
gene expression datasets from Lymphoma, Liver and Leukaemia datasets. Three different classifiers
namely Support vector machines-one against all (SVM-OAA), K nearest neighbour (KNN) and Linear
Discriminant analysis (LDA) have been tested and the results have indicated improvement in
performance of SVM-OAA classifier with satisfactory results on all the three datasets when compared
to the other two classifiers.
Chhanda Ray [27] has discussed DNA micro array gene expression patterns of several model
organisms and provided a fascinating opportunity to explore important abnormal biological
phenomena. The development of cancer has been a multi-step process in which several genes and
other environmental and hormonal factors play an important role. They have proposed a new
algorithm to analyze DNA micro array gene expression patterns efficiently for huge amount of DNA
micro array data. For better visibility and understanding, experimental results of DNA micro array
gene pattern analysis have been represented graphically. The shape of each graph corresponding to
a DNA micro array gene expression pattern has been determined by using an eight-directional chain
code sequence, which has been invariant to translation, scaling, and rotation. The cancer
development has been identified based on the variations of DNA micro array gene expression
patterns of the same organism by simultaneously monitoring the expressions of thousands of genes.
At the end, classification of cancer genes has also been focused based on the distribution probability
of codes of the eight-directional chain code sequences representing DNA micro array gene
expression patterns and the experimental result has been provided.
While Chu et al. [31] [36]has used a five-genes set for 100% correct classification on the lymphoma
data in the fuzzy NF framework, A dynamic fuzzy neural network, involving self-generation,parameter
optimization, and rulebase simplification, is used [31][36] for the classification of cancer data such as
lymphoma,liver cancer.
Banerjee et al. [35] [36]obtained a misclassification for just two samples from the test data using a
two-genes set. In case of the leukemia data, a two-genes set is selected, whereas the colon data
results in an eight-genes reduct size. An evolutionary rough feature selection algorithm [35][36] has
been used for classifying microarray gene expression patterns. The effectiveness of the algorithm is
demonstrated on three cancer datasets, viz., colon, lymphoma, and leukemia
S.Mitra et al. Has discussed An evolutionary rough c-means clustering algorithm applied to microarray
gene expression data [28][36]. RSes are used to model the clusters in terms of upper and lower
approximation.
S.Bicciato et.al. discussed An autoassociative neural network for simultaneous pattern identification,
feature extraction, and classification of gene expression data [30] Results are demonstrated on
leukemia and colon cancer20 datasets. The identification of gene subsets for classifying two-class

disease samples has been modeled as a multiobjective evolutionary optimization problem by K.Deb
et.al. [32], involving minimization of gene subset size to achieve reliable and accurate classification
based on their expression levels. Classification of acute leukemia, having highly similar appearance in
gene expression data, has been made by combining a pair of classifiers trained with mutually
exclusive features [29].
RSes have been applied mainly tomicroarray gene expression data, in mining tasks like classification
[38], [33], H.Midelfart .et.al usedClassification rules (in if–then form) for extracting data from
microarray data [38], using RSes with supervised learning. Gastric tumor classification in microarray
data is made using rough set-based learning [33].
4. ASSEMBLANCE OF SOFT COMPUTING TECHNIQUES WITH MICROARRAY
TECHNOLOGY IN CANCER BIOLOGY
The esemblence of soft computing techniques applied to Microarray Technology in Cancer Biology is
described in table1. The table also gives the information of different datamining tasks involved.
5. CONCLUSION
Microarray technology technology has suppressed the conventional cancer diagnostic methods based
on the morphological appearance of the cancerous cell which quiet often were misdiagnosed.
For more precision and effective results the emboiding of the different soft computing approaches is
really recommendable.Based on the numerous publications investigating the use of DNA microarray
technology ,data mining and soft computing to predict outcome in different cancer sites, this
technology seems to be the most mature technology from all the omics.
Modelling/data mining tasks Result obtained Reference

TABLE 1: Use of Microarray Technology with Softcomputing in Cancer Research
Class Discovery methods. Assigning tumors to known classes. [34]
2-way Clustering Both genes & tumors were clustered [38]
Hirarchical clustering Found different groups of Breast cancer. [35]
Nearest Shrunken centroid
method (PAM)
Limit on the number of genes necessary to
prediction.
[39]
ANN & DCT Very high success rate for classification of
tumor and non tumors
[22]
Supervised machine learning High accuracy with only two genes [23]
Manifold learning method Efficient discriminant feature extraction and
gene expression data classification.
[24]
Rough sets ,feature selection Superior in applicability and robustness. [25]
Gene ranking and gene subset
ranking
Improved classification performance [26]
ANN,classification Simultaneous pattern extraction,Leukemia
classification
[29][30]
GA,classification reliable and accurate classification based on
their expression levels,minimization of gene
subset size
[32]
NF,feature selection Feature selection [32]
Fuzzy NN(dynamic structure
growing),feature selection.
ANN,classifiers
Colon classification,Classification of acute
leukemia, having highly similar appearance in
gene expression data
[30][32]
RS+GA,clustering effectiveness of the algorithm is demonstrated
on three cancer datasets, viz., colon,
lymphoma, and leukemia.
[28]
NF,self-generation, parameter
optimization, and rulebase
simplification,classification,feature
selection.
Lymphoma classification. [31]
NF,rule base simplification. Classification of Small round blue cell tumor [31]
NF,classification Liver cancer100% correct classification on the
lymphoma data
[31]
RS+GA,classification Gastric tumor classification [33]
GA(multi objective approach) Lung cancer & mixed lineage leukemia more
efficient resuls in gene selection as compared
to single objective
[37]
GA ,SVM Multiclass cancer categarization [38]

REFERENCES
[1] Nguyen and Rocke, Classification of Acute Leukemia based on DNA Micro array Gene
Expressions using Partial Least Squares, Kluwer Academic, Dordrecht, 2001
[2] Jian J. Dai, Linh Lieu, and David Rocke, "Dimension Reduction for Classification with Gene
Expression Micro array Data", Statistical Applications in Genetics and Molecular Biology: Vol. 5,
No. 1, 2006
[3] Alok Sharma and Kuldip K. Paliwal, "Cancer classification by gradient LDA technique using
micro array gene expression data", Data & Knowledge Engineering, Vol. 66, pp. 338-347, 2008
[4] Chun-Hou Zheng, Bo Li, Lei Zhang and Hong-Qiang Wang, "Locally Linear Discriminant
Embedding for Tumor Classification", In Proceedings of ICIC, pp.1093-1100, 2008
[5] Cheng-San Yang, Li-Yeh Chuang, Chao-Hsuan Ke and Cheng-Hong Yang, "A hybrid Feature
Selection Method for Micro array Classification", International Journal of Computer Science,
Vol. 35, No. 3, 2008
[6] Danh V. Nguyen, David M. Rocke, "Tumor Classification by Partial Least Squares Using Micro
array Gene Expression Data", Bioinformatics, Vol. 18, No. 1, pp. 39-50, 2002
[7] Pengyi Yang and Zili Zhang, "An Embedded Two-Layer Feature Selection Approach for
Microarray Data Analysis", IEEE Intelligent Informatics Bulletin, Vol.10, No.1, pp. 24-32, 2009
[8] Yuh-Jye Lee and Chia-Huang Chao, "A Data Mining Application to Leukemia Micro array Gene
Expression Data Analysis", International Conference on Informatics, Cybernetics and Systems
(ICICS), Kaohsiung, Taiwan, 2003
[9] James J. Chen and Chun-Houh Chen, "Micro array Gene Expression", Encyclopedia of
Biopharmaceutical Statistics, 2nd Edition, Marcel Dekker, Inc., pp. 599-613, 2003
[10] Seeja and Shweta, "Microarray Data Classification Using Support Vector Machine", International
Journal of Biometrics and Bioinformatics (IJBB), Vol. 5, No. 1, pp. 10-15, 2011
[11] Yee Hwa Yang and Natalie P. Thorne, "Normalization for Two-color cDNA Microarray Data",
Science and Statistics: A Festschrift for Terry Speed, Vol. 40, pp. 403-418, 2003
[12] Fei Pana, Baoying Wanga, Xin Hub and William Perrizoa, "Comprehensive vertical sample-
based KNN/LSVM classification for gene expression analysis", Journal of Biomedical
Informatics, Vol. 37, pp. 240–248, 2004. Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C.,
Gassenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield,
C.D., Lander, E.S., “Molecular classification of cancer: class discovery and class prediction by
gene expression monitoring”, Science, 286(15):531–537, 1999.
[13] Terrence S. Furey, Nello Cristianini, Nigel Duffy, David W. Bednarski, Michèl Schummer, and
David Haussler, “Support vector machine classification and validation of cancer tissue samples
using microarray expression data “, Bioinformatics6(10): 906-914 , 2000
[14] Zhang, X. and Ke, H.,” ALL/AML cancer classification by gene expression data using SVM and
CSVM approach”, Genome Informatics, Universal Academy Press, pp. 237-239, 2000.
[15] Xin Zhao, Leo Wang-Kit Cheung, “Kernel-imbedded Gaussian processes for disease
classification using microarray gene expression data”, BMC Bioinformatics.,8:67,2007.

[16] Wenlong Xu, Minghui Wang, Xianghua Zhang, Lirong Wang, Huanqing Feng,” SDED: Anovel
filter method for cancer-related gene selection”, Bioinformation 2(7): 301-303,2008.
[17] D.P. Berrar, C.S. Downes, W. Dubitzky, “Multiclass Cancer Classification Using Gene
Expression Profiling and Probabilistic Neural Networks”, Pacific Symposium on Biocomputing
8:5-16, 2003.
[18] Golub TR, Slonim DK, Tamayo P, et al. Molecular classifi cation of cancer: class discovery and
class prediction by gene expression monitoring. Science 1999 ; 286 : 531 -7
[19] Perou CM, SÃ¸rlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000
406 : 747 -52
[20] Alon U, Barkai N, Notterman DA, et al. Broad patterns of gene expression revealed by clustering
analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad
Sci USA 1999 ; 96 : 6745 -50
[21]. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken
centroids of gene expression. Proc Natl Acad Sci USA 2002 ; 99 : 6567 -72
[22] Ahmad M. Sarhan, "Cancer Classification Based on Micro array Gene Expression Data Using
DCT and ANN", Journal of Theoretical and Applied Information Technology, Vol. 6, No. 2, pp.
208-216, 2009
[23] Bharathi and Natarajan, "Cancer Classification of Bioinformatics data using ANOVA",
International Journal of Computer Theory and Engineering, Vol. 2, No. 3, pp. 369-373, June
2010
[24] Bo Li, Chun-Hou Zheng, De-Shuang Huang, Lei Zhang and Kyungsook Han, “"Gene expression
data classification using locally linear discriminant embedding", Computers in Biology and
Medicine, Vol. 40, pp. 802–810, 2010
[25] Xiaosheng Wang and Osamu Gotoh, "A Robust Gene Selection Method for Micro array-based
Cancer Classification", Journal of Cancer Informatics, Vol. 9, pp. 15-30, 2010
[26] Mallika and Saravanan, "An SVM based Classification Method for Cancer Data using Minimum
Micro array Gene Expressions", World Academy of Science, Engineering and Technology, Vol.
62, No. 99, pp. 543-547, 2010
[27] Chhanda Ray, "Cancer Identification and Gene Classification using DNA Micro array Gene
Expression Patterns", International Journal of Computer Science Issues, Vol. 8, Issue 2, pp.
155-160, March 2011.
[28] S. Mitra, “An evolutionary rough partitive clustering,” Pattern Recognit. Lett., vol. 25, pp. 1439–
1449, 2004
[29] S. B. Cho and J. Ryu, “Classifying gene expression data of cancer using classifier ensemble
with mutually exclusive features,” Proc. IEEE, vol. 90, no. 11, pp. 1744–1753, Nov. 2002.
[30] S. Bicciato,M. Pandin, G. Didon`e, andC.DiBello, “Pattern identification and classification in
gene expression data using an autoassociative neural network model,” Biotechnol. Bioeng., vol.
81, pp. 594–606, 2003.
[31] F. Chu, W. Xie, and L. Wang, “Gene selection and cancer classification using a fuzzy neural
network,” in Proc. 2004 Annu. Meet. North Amer. Fuzzy Information Processing Soc. (NAFIPS),
vol. 2, pp. 555–559.

[32] K. Deb and A. Raji Reddy, “Reliable classification of two-class cancer data using evolutionary
algorithms,” BioSystems, vol. 72, pp. 111–129, 2003.
[33] H. Midelfart, J. Komorowski, K. Nørsett, F. Yadetie, A. K. Sandvik,and A. Lægreid, “Learning
rough set classifiers from gene expression and clinical data,” Fundamenta Inf., vol. 53, pp. 155–
183, 2002.
[34] M. E. Futschik, A. Reeve, and N. Kasabov, “Evolving connectionist systems for knowledge
discovery from gene expression data of cancer tissue,” Artif. Intell. Med., vol. 28, pp. 165–189,
2003.
[35] M. Banerjee, S. Mitra, and H. Banka, “Evolutionary-rough feature selection in gene expression
data,” IEEE Trans. Syst., Man, Cybern. C, Appl.
[36] S.Mitra, YHaya.shi,Bioinformatics with softcomputing” IEEE TRANSACTIONS ON SYSTEMS,
MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 36, NO. 5,
SEPTEMBER 2006.
[37] Fei Pana, Baoying Wanga, Xin Hub and William Perrizoa, "Comprehensive vertical sample-
based KNN/LSVM classification for gene expression analysis", Journal of Biomedical
Informatics, Vol. 37, pp. 240–248, 2004
[38] H. Midelfart, A. Lægreid, and J. Komorowski, Classification of Gene Expression Data in an
Ontology, vol. 2199. Lecture Notes in Computer Science, Berlin, Germany: Springer-Verlag,
2001, pp. 186–194

Application of Microarray Technology and softcomputing in cancer Biology

More Related Content

What's hot (20)

Similar to Application of Microarray Technology and softcomputing in cancer Biology (20)

Recently uploaded (20)

Application of Microarray Technology and softcomputing in cancer Biology