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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
Core for: None
Prerequisites: No Prerequisites
Catalogue: Editor's Office
UCI Course Enrollment History
Year Fall Winter Spring Summer
2012   Eric Mjolsness
2010     Pierre Baldi