1. CSE 494/CSE 598/CBS 598 Application of AI to molecular Biology (4:40 – 5: 55 PM, BYAC 190) Instructor: Chitta Baral Office hours: TTh 3 to 4 PM

2. Four Great Questions The nature of matter. The origins of the universe. The nature of life. The workings of mind (simulating intelligence artificially).

3. Meaning of the word: intelligence 1. (a) The capacity to acquire and apply knowledge. (b) The faculty of thought and reason. (c) Superior powers of mind. See Synonyms at mind. 2. An intelligent, incorporeal being, especially an angel. 3. Information; news. See Synonyms at news. 4. (a) Secret information, especially about an actual or potential enemy. … Source: The American Heritage® Dictionary

4. Meaning of the word: intelligence n. 1. The capacity to acquire and apply knowledge, especially toward a purposeful goal. 2. An individual's relative standing on two quantitative indices, namely measured intelligence, as expressed by an intelligence quotient, and effectiveness of adaptive behavior. The American Heritage® Stedman's Medical Dictionary

5. Meaning of the word: intelligence n. 1 a : the ability to learn or understand or to deal with new or trying situations b : the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests) 2 : mental acuteness Merriam-Webster's Medical Dictionary

6. Meaning of the word: intelligence n. 1. the ability to comprehend; to understand and profit from experience [ant: stupidity] 2. a unit responsible for gathering and interpreting intelligence 3. secret information about an enemy (or potential enemy); we sent out planes to gather intelligence on their radar coverage Source: WordNet ® 1.6, © 1997 Princeton University

7. The key features of an intelligent entity it can acquire knowledge through various means such as learning from experience, observations, reading and processing natural language text, from discussion with others it can reason with this knowledge to make plans, explain observations, achieve goals, etc.

8. AI and molecular biology This course is about the application of the above science and engineering (referred to as AI) to molecular biology.

9. Molecular Biology molecular biology n. The branch of biology that deals with the formation, structure, and function of macromolecules essential to life, such as nucleic acids and proteins, and especially with their role in cell replication and the transmission of genetic information. The branch of biology that deals with the manipulation of DNA so that it can be sequenced or mutated. If mutated, the DNA is often inserted into the genome of an organism to study the biological effects of the mutation. Source: The American Heritage® Dictionary of the English Language, Fourth Edition Copyright © 2000 by Houghton Mifflin Company. Published by Houghton Mifflin Company. All rights reserved. n : the branch of biology that studies the structure and activity of macromolecules essential to life (and especially with their genetic role) Source: WordNet ® 1.6, © 1997 Princeton University

10. Nature of life Source of diseases and disorders -- often traced to activities inside cells. The activities inside cells are often regulated by proteins (enzymes, ligands on cell surfaces, etc.) Central Dogma: DNA (genes) RNA Proteins Genome: The whole set of genes Differential gene expression Q: When are particular genes expressed in a cell Q: The details of the various interactions Q: Reasoning about the interactions

11. Main themes of the course How to acquire/learn molecular biology knowledge? How to do various kinds of reasoning with such knowledge? (Why reason with such knowledge?)

12. Learning biological knowledge and meta-knowledge (ontologies) From observations (microarray, gene profile data) From reading Intex (protein-gene1 interacts with protein-gene2) Other kinds of information extraction TREC-Genomics BioQA From discussing CBioc, CBioc-I Collaborative filtering

14. Signal Pathways (from http://www.afcs.org/cm2/)

15. Reasoning-I Reasoning about interactions Prediction Side effects of drugs Planning Drug and therapy design Explanation, Diagnosis Explaining unusual behavior of cells Hypothesizing missing knowledge about cell behavior

16. Reasoning - II Reasoning about consistency of Ontologies Reasoning across various kinds of knowledge From interaction knowledge, gene disease relationship, drug effect data and other knowledge to drug-disease predictions.

17. Tentative topics to be covered Introduction Overview of Molecular Biology Ontologies Learning Knowledge (from text) Learning interactions, etc. Learning Ontologies Learning Knowledge (from data) Learning causality Dynamic Bayes nets Representation and reasoning with biological knowledge Reasoning with ontologies Overview of other applications of AI to molecular biology More on Hidden Markov Models Use of decision trees, inductive Logic programming (Progol), etc. for classification and prediction. Gene finding, protein folding, kernel methods, protein 3D structure prediction

18. Grading and Modus Operandi project + paper + class presentations 80% Chance to collaborate with my Ph.D students Expected to be of publication quality Class Test (April 3 rd week) 20% Modus Operandi: There will be 8-9 groups each of 1-2 students Groups select project asap (in two weeks) First 5 classes I will present We will have some guest lectures Other classes presented by my Ph.D students Group discussion on PSB topics.

19. Projects Each project is of research interest to ASU and TGen researchers, particularly to me. Students will work closely with me, my colleague Dr. Graciela Gonzalez and my Ph.D students

20. Tentative list of projects – 1 AI, KR and Ontology issues in BioPAX and possible solutions. – Luis, Nam, Jicheng Various kinds of knowledge extraction from natural language text (abstracts and articles) -- Luis, Graciela Protein/gene Interactions Knowledge about images Etc. Extracting ontologies Hypothesis formation/generation -- Nam Reasoning with various kinds of data – Luis, Xin, Nam Modeling of pathways – Nam, Jicheng Qualitative modeling Quantitative modeling

21. Tentative list of projects – 2 Biological Question answering -- Luis Learning gene interactions (as Bayes nets or a similar structure) -- Xin from time series micro-array data From gene profile data From multiple data types Any idea from PSB topics. You may suggest and discuss a topic, but need to do it asap

22. My current projects Biosignet: http://www.biosignet.orghttp://www.biosignet.org CBioC: http://www.cbioc.orghttp://www.cbioc.org InteEx BioQA: http://www.bioqa.orghttp://www.bioqa.org TREC-Genomics Biogenenet: http://www.biogenenet.org