Mining the Genome

1. Mining the Genome Filip Železný ČVUT FEL, Prague Dept. of Cybernetics Gerstner Laboratory

2. Intro • Research at ČVUT FEL Dept. of Cybernetics • Nature Inspired Technologies • machine learning • evolutionary computation • Agent Computing • Robotics • Computer Vision • EU Projects (6 FP) • 14 running in 2005, 9 new starting 2006

3. Machine Learning basics

4. Machine Learning & Data Mining • Supervised learning • given examples and their class labels • find a model for predicting class labels of new examples • also: “concept learning”, “predictive classification”, ... • Example • Given: • Discover: size=small & luxury=low  affordable

5. Machine Learning Plethora of paradigms Decision trees Artificial NeuralNetworks Support VectorMachines “Symbolic” “Subsymbolic” “Statistical” Learning = optimization in structure / parameter space Learning = search AI techniques employed (gradient descent, heuristic search)

6. Relational Learning What if examples have a structure? Not an attribute tuple ! Description spread in multiple tables of a relational database

7. Relational Learning • Relational learning • Representing data and rules in relational logic (Prolog) • Exploits background knowledge (eg. “charge”) • Inductive Logic Programming carcinogenic(Compound) IF has_atom(Compound, Atom) & type(Atom, carbon) & charge(Atom, Charge) & Charge > 0.0133 & has_atom(Compound, Atom2) & double_bond(Atom1, Atom2)

8. Applications of Interest 3 hot fields intersection BIOtechnologies(genomics) INFORMATIONtechnologies(machine learning) NANOtechnologies(microarray chips)

9. A quick intro into computational genomics

10. Background: GENETICS How does a cell know what to do?

11. Chromosomes Chromosomes get copied during mitosis They carry the assembly instructions? How? Chromosomes = proteins + DNA where is the information ??

12. DNA 1953: Jim Watson & Francis Crick Discover the DNA structure. That is where the information is. 4-symbol alphabet Guanin, Adenin, Cytosin, Tymin Double-helix pairing: C-G A-T video

13. The CENTRAL DOGMA of Molecular Biology • Gene = DNA subsequence • Genes code for proteins • Gene expression • DNA piece transcribes to RNA • RNA translates into a protein • Proteins `do the job’ • - enzymes • - building blocks • - ... video

14. Protein Coding Codon(3 bases) DNA strand aminoacid Protein

15. Protein structures “resolution”

16. Secondary structure prediction Two common secondary structures  - sheet  - helix Primary structure determines secondary structure. Computational problem:Given primary structure, predict if  - sheet or  - helix NOBODY CAN DO THAT !

17. Secondary structure prediction • Secondary structure prediction with ILP [Muggleton 1992] Using ILP, obtained rulessuch as alpha0(A,B)  ... position(A,D,O) & not_aromatic(O) & small_or_polar(O) & position(A,B,C) & very_hydrophobic(C) & not_aromatic(C) ...etc (22 literals) • Note the incorporation of background knowledge • Accuracy 81%, best at the time • Published in JrProtein Engineering

18. Sequencing the Human Genome

19. The Genome project • 1993 – 2003 All human genes sequenced Celera X NIH race • Challenge NOW: annotate the genes • discover functions • interactions • dynamic pathways video

20. Genomics research Verification(targeted assay) Human intuition Hypotheses • Traditional functional genomics research • Hypothesis - driven • eg. a gene is suspected to be responsible for ... • then tracing its expression in relevant tissues • “First hypothesize, then measure”

21. Gene Expression Microarrays • Microarray chip: • Measures expression of tens of thousands genes simultaneously: “high-throughput” • pioneering technology (mid to late 90’s) • A grid carrying synthesized DNA probes •  Breakthrough in genomics research? photo scan

22. Genomics Research • High-Throughput approach to functional genomics ? • Data-driven, unbiased, “First measure, then hypothesize” • Might reveal never-thought-of relationships Microarray data Human analysis Hypotheses IMPOSSIBLE (TOO MUCH DATA) Expression of almost entire genome(tens of thousands genes)

23. Genomics Research through Machine Learning • AI based High-Throughput functional genomics ? High-throughputscreening High-performancecomputing Microarray data Machine Learning Hypotheses Interpretation

24. Genomics Research with AI • This concept has recently been proven to work • Golub et al., Science286:531-537 1999 • leukemia classification model (AML vs. ALL) • voting of informative attributes (genes) • Discovery of new classes (clustering) • Ramaswamy et al., PNAS 98:15149-54 2001 • Tumor classification • 14 classes of cancer • used Support Vector Machines video

25. Interpretable classifiers • Comprehensibility Pursuit: Rule Based Models • Models interpretable by biologists • Our work • D. Gamberger, N. Lavrač, F. Železný, J. Tolar Jr Biomed Informatics 37(5):269-284 2004 IF gene_20056 EXPRESSEDAND gene_23984 NOT_EXPRESSEDTHEN cancer_class = AML Class

26. Exploiting Background knowledge • Tons of genomic background knowledge available • Relational learning would allow to exploit it!

27. Relational Genomic Data Mining • Our current work Combining expression & gene annotation data Rule Based Model

28. Relational Genomic Data Mining • Example rule algorithmically discovered • ... open end, no conclusions expressed_in_all(Gene) IF has_location(Gene, integral_to_membrane) & has_function(Gene, receptor_activity) Expression of genescoding for proteinslocated in the integral to membrane cell component,whose functions include receptor activity, has a high correlation with the BCR class of acute lymphoblastic leukemia (ALL) and a low correlation with other classes of ALL.