Overview | Syllabus & Lecture Notes | Resources
The purpose of this course is to serve as a brief introduction to computational and statistical methods in biology. We will discuss a breadth of algorithms for analyzing and organizing biological data. This includes biological sequence alignment and analysis, gene expression and genetic marker analysis using both "unsupervised" and "supervised" machine learning techniques, protein structure prediction, and even data mining from the biomedical literature.
The workshop is part of the ISB Summer Research Program, in the Computational Biology & Biostatistics track.
May 29 & 30*, June 2, 4*, & 5, 10:00-11:30am
Room 1210 (*1217c) Medical Sciences Center [map].
Lecture notes and some reading materials can be downloaded here in Adobe PDF format. Lectures are based on the notes of Mark Craven, Michael Molla, Burr Settles, and Ameet Soni.
|Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids.
R. Durbin, S.R. Eddy, A. Krogh, & G. Mitchison. Cambridge University Press, 1998.
An introduction and overview for probabilistic models of proteins and nucleic acids.
|Foundations of Statistical Natural Language Processing.
C.D. Manning & H. Schutze. MIT Press, 2001.
An excellent introduction to NLP algorithms, many of which are also used in computational biology applications.