The application of artificial intelligence
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The document discusses the application of artificial intelligence techniques in bioinformatics, providing examples of how symbolic machine learning, nearest neighbor approaches, clustering, and identification trees can be used. It also outlines some of the major challenges in applying AI to bioinformatics, including the need for cross-disciplinary collaboration and developing methods to intelligently analyze and interpret the large amounts of biological data being generated.
The application of artificial intelligence
- 1. The application of artificial intelligence Presented by: Pallavi Vashistha techniques in bioinformatics
- 2. Outline • Bioinformatics Today • Artificial Intelligence application • Examples: Symbolic machine learning Nearest neighbour approach Clustering Identification trees • Major Challenge and Research Issues
- 3. History of Bioinformatics Year Subject Name MBP (Millions of base pairs) 1995 Haemophilus Influenza 1.8 1996 Bakers Yeast 12.1 1997 E.Coli 4.7 2000 Pseudomonas aeruginosa 6.3 A. Thaliana 100 D. Melonagaster 180 2001 Human Genome 3,000 2002 House Mouse 2,500
- 4. Bioinformatics Today Sequence analysis Sequence alignment Structure and function prediction Gene finding Structure analysis Protein structure comparison Protein structure prediction RNA structure modeling Expression analysis Gene expression analysis Gene clustering Pathway analysis Metabolic pathway Regulatory networks 4
- 5. Artificial Intelligence application There are several important problems where AI approaches are particularly promising • Prediction of Protein Structure • Semiautomatic drug design • Knowledge acquisition from genetic data
- 6. Artificial Intelligence application How to classify biological sequences • SVM(support vector machine ), Neural Nets, Decision Trees, Rules How to cluster biological entities • Bi-clustering, K-means, hierarchical How to select features • LDR (Linear Discriminant Analysis), PCA (Principal Components Analysis), SVM-RFE (recursive feature elimination)
- 7. Nearest neighbour approach 0 Decision tree: • each node is connected to a set of possible answers, • each non-leaf node is connected to a test which splits its set of possible answers into subsets corresponding to different test results, • each branch carries a particular test result’s subset to another node.
- 8. Nearest neighbour approach Example: Solution: 0 Example: To see how 0 To answer this question, decision trees are useful for we need to assume a nearest neighbour consistency heuristic, as calculations, consider 8 follows. Find the most blocks of known width, height and colour (Winston, similar case, as 1992). A new block then measured by known appears of known size but properties, for which the unknown colour. On the property is known; then basis of existing guess that the unknown information, can we make property is the same as an informed guess as to the known property. This what the colour of the new is the basis of all nearest block is? neighbour calculations.
- 12. Clustering 0 Clustering follows the principles of nearest neighbour calculations but attempts to look at all the attributes (positions) of biosequences rather than just one attribute (position) for identifying similarities. 0 This is achieved typically by averaging the amount of similarity between two biosequences across all attributes. 0 For example, imagine that we have a table of information concerning four organisms with five characteristics:
- 13. • Given this table, can we calculate how similar each organism is to every other organism? • The nearest neighbour approach described earlier would work through the attributes(‘characteristics’) one at a time. For short bio sequences this may be feasible, but for bio sequences with hundreds of attributes (e.g. DNA bases) this is not desirable, since we could probably classify all the samples with just the first few attributes
- 14. Clustering can be demonstrated in the following way: 0 The first step is to calculate a simplematching coefficient for every pair of organisms in the table across all attributes. 0 For instance, the matching coefficient for A and B is the number of identical characteristics divided by the total number of characteristics, 0 4/5 = 0.8 (1+0+1+1+1=4/5=0.8). Similarly, 0 A and C = 0.4 (0+0+0+1+1 =2/5 = 0.4) 0 A and D = 0.2 (0+0+0+0+1 = 1/5 = 0.2) 0 B and C = 0.6 (0+1+0+1+1 = 3/5 = 0.6) 0 B and D = 0.4 (0+1+0+0+1 = 2/5 = 0.4) 0 C and D = 0.8 (1+1+1+0+1 = 4/5 = 0.8)
- 15. • We then find the first highest matching coefficient to form the first 'cluster'of similar bacteria. Since we have two candidates here (AB and CD both have 0.8), we randomly choose one cluster to start the process: AB. • The steps are then repeated, using AB as one ‘organism’ and taking partial matches into account. • the average matching coefficient for AB and C = 0.5 (0+0.5+0+1+1 = 2.5/5 = 0.5) where the 0.5 second match within the parentheses refers to C sharing its second feature with B but not A. • The matching coefficients for AB and D = 0.3 (0+0.5+0+0+1 = 1.5/5 = 0.3) and for C and D = 0.8 (as before). • Since C and D have the highest cooefficient, they form the second cluster.
- 16. Finally, we calculate the average matching coefficients for the new 'clusters'of organism taking AB as one organism and CD as another organism = 0.4 (0+0.5+0+0.5+1 = 2/5 = 0.4) again taking partial matches into account. We can then construct a similarity tree using these coefficients, as follows:
- 17. Identification tree The task now is to determine which of the attributes contribute towards someone being sunburned or not. First, we need to introduce a disorder formula and associated log values to rank attributes in terms of their influence on who is and who isn’t sunburned.
- 18. where nb is the number of samples in branch b, nt is the total number of samples in all branches, and nbc is the total of samples in branch b of class c. • The idea is to divide samples into subsets in which as many of the samples have the same classification as possible (as homogeneous subsets as possible). The higher the disorder value, the less homogeneous the classification. • We now work through each attribute in turn, identifying which of the samples fall within the branches (attribute values) of that attribute, and signify into which class each of the samples falls
- 24. Given the full identification tree, we can then derive rules by following all paths from the root to the leaf nodes, as follows: 0 (a) If a person’s hair colour is brown, then the person is not sunburned. 0 (b) If a person’s hair colour is red, then the person is sunburned. 0 (c) If a person’s hair colour is blond and that person has used sun tan lotion, then the person is not sunburned. 0 (d) If a person’s hair colour is blond and that person has not used sun tan lotion, then the person is sunburned.
- 25. Major Challenges and Research Issues • Requires individuals with knowledge of both disciplines • Requires collaboration of individuals from diverse disciplines
- 26. Major Challenges and Research Issues • Data generation in biology/bioinformatics is outpacing methods of data analysis • Data interpretation and generation of hypotheses requires intelligence • AI offers established methods for knowledge representation and “intelligent” data interpretation