StatsM254_Syllabus_10F

StatsM254_Syllabus_10F - 2 Multivariate Methods gene...

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Stats M254 Statistical Methods in Computational Biology Course site: http://www.stat.ucla.edu/ zhou/courses/Stats254 Lecture: WF 3–4:20pm, MS 5128. Instructor: Qing Zhou ([email protected]), OH: Wed 4:20-5:30pm, MS 8979. Prerequisite: Stats 100A (Probability) or Stats 200A (Probability) or Bioinfo 260A (Bioinfor- matics). Grading Your final grade of this course will be composed of two parts: 1. Homework assignments (60%). We will have three assignments. Some problems need computer programming. 2. Group oral presentation (40%). Each group (2-3 students) will give a 40-minute presenta- tion on a recent research paper. Topics Introduction to statistical and computational methods in computational biology and bioinfor- matics. The topics are grouped into four chapters: 1. Introduction and Data: molecular biology of gene regulation, typical data.
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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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