The course covers computational approaches to understanding and predicting the structure, function, interactions, and evolution of DNA, RNA, proteins, and related molecules and processes. The emphasis is on providing a unified Bayesian statistical framework to mine large noisy data sets that are becoming the hallmark of modern biology. Topics include HMMs, gene finding, probabilistic models of evolution and phylogenetic tree reconstruction, stochastic grammars and RNA modeling, protein secondary structure prediction, and analysis of gene expression. A unified Bayesian probabilistic framework for modeling and mining biological data. Course assumes some background in biology, and basic knowledge of probability, statistics, and programming .
Additional assignments can include homeworks .
Schedule may change to follow class interest, schedule outside speakers, accommodate unforeseen opportunities or exigencies, etc. Gene Finding Algorithms. Stochastic Grammars and Languages. Beyond Context Free Grammars. Machine Learning Approaches for Protein Secondary Structure Prediction.