The seminar will cover selected topics in intelligent systems in molecular biology including:

• Molecular Biology and Genetics for Non-Biologists.
• Some Computational Challenges in Genomics and Proteomics
• Computer Science for Biologists
• Design, Implementation, and Use of Biological Databases (e.g., genome and protein databases)
• Selective Information Retrieval from Biological Databases
• Biological Data Transformation, Representation, and Information Extraction
• Data-driven knowledge discovery and theory refinement from Biological Data
• Computational Biology applications of Artificial Intelligence (e.g., data mining, intelligent agents).

The material will be based on recent papers from the Intelligent Systems in Molecular Biology Conference, Computational Biology journal, the Pacific Symposium on Biocomputing, and related sources.

Interested students can register for 1-3 Credits depending on their level of interest and anticipated level of participation:

• 1 credit: lead the discussion on a paper, a book chapter, or a small set of papers on a given topic.
• 3 credits: in addition to reading and leading the discussion on assigned material, complete a small group research project on intelligent systems in molecular biology.

• Overview of Data-Driven Knowledge Discovery and Theory Refinement for Biologists. V. Honavar.
  Suggested Readings:
  • Aha, D. (1995). Machine Learning (tutorial).
  • Balakrishnan, K. and Honavar, V. (1997). Intelligent Diagnosis Systems. Journal of Intelligent Systems. In press.
  • Honavar, V. (1994). Toward Learning Systems That Use Multiple Strategies and Representations. In: Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. pp. 615-644.
  • Honavar, V. (1998). Machine Learning. Invited article. In: Encyclopedia of Electrical and Electronic Engineering. Webster, J. (Ed.), New York: Wiley. In press.
  • Honavar, V. (1998). Inductive Learning: Principles and Applications. Invited chapter. In: Intelligent Data Analysis in Science. Cartwright, H. (Ed.) London: Oxford University Press. To appear.
  • Langley, P. Elements of Machine Learning. Palo Alto, CA: Morgan Kaufmann.
  • Mitchell, T. (1997). Machine Learning. New York: Addison Wesley.
  • Mitchell, T. (1997). Does machine learning really work? AI magazine, Vol. 18. No. 3. pp. 11-20.
• Overview of Molecular Biology and Genetics for Computer Scientists. D. Dobbs.
  Suggested Readings:
  • Casey, D. (1992). Primer on Molecular Genetics, U.S. Department of Energy.
  • Waterman, M.. (1995). Introduction to Computational Biology: Maps, Sequences, and Genomes New York: Chapman & Hall. (chapter 1)
  • Watson et. al. (1987). Molecular Biology of the Gene. Vol. 1. New York: Benjamin Cummings.
  • Singer, M. & Berg, P. (1991). Genes and Genomes. Mill Valley, CA: University Science Books.
  • Davis, C. (1998). Introduction to Molecular and Cell Biology University of Western Kentucky.
  • Biochemistry Course from University of Kansas Introduction to DNA Structure, University of Arizona.
• Machine Learning approaches to Gene Identification. V. Honavar.
  Suggested Readings
  • Craven, M. and Shavlik, J. (1994). Machine Learning Approaches to Gene Recognition. IEEE Expert. 1994.