CS 284B: Probabilistic Modeling of Biological Data
A unified Bayesian probabilistic framework for modeling and mining biological data. Applications range from sequence (DNA, RNA, proteins) to gene expression data. Graphical models, Markov models, stochastic grammars, structure prediction, gene finding, evolution, DNA arrays, single- and multiple-gene analysis. Prerequisite: a basic course in algorithms and molecular biology, or CS 284A or equivalent, or consent of instructor. Concurrent with CS 184B.
Level: Graduate Units: 4 Category: Core for: None Prerequisites: No Prerequisites Catalogue: Editor's Office