Unformatted text preview: 2. Multivariate Methods: gene expression data, multiple tests, principal component analysis, clustering methods, liquid association. 3. Statistical Sequence Analysis: Bayesian inference, Markov chain, hidden Markov model, missing data, Monte Carlo, motif discovery, sequence segmentation. 4. Predictive modeling: Bayes classiﬁer, discriminant analysis, logistic regression, support vector machine, boosting, sparse modeling, and their applications in gene regulation. References • Ewens, W.J. and Grant, G.R. Statistical methods in bioinformatics: An introduction. • Hastie, T. et al. The elements of statistical learning. • Watson, J.D. et al. Molecular biology of the gene. • Other papers posted on the course webpage. 1...
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This note was uploaded on 11/24/2010 for the course STAT 201a taught by Professor Wu during the Spring '10 term at Pasadena City College.
- Spring '10