Course Title:
Statistics for Bioinformatics
Course Description:
Introduces the concepts of probability and statistics and the statistical concepts used in genomics (sequence alignment algorithms, mapping gene, and protein stochastic networks) and in drug discovery and evaluation. Methods include theoretical approaches such as maximum likelihood, entropy maximization, minimal description length, and empirical methods based on clustering, pattern recognition, bootstrapping, neural networks, Markov chain Monte Carlo, fitting Markov models of local interactions, and Bayesian models. Discusses application examples of discriminant analysis, principal components, multiple correlation, regression, and design of experiments to bioinformatics.
Fall Offering:
Lab/Coreq 1:
Spring Offering:
Lab/Coreq 2:
Summer Offering:
Lab/Coreq Remarks:
Summer 1 Offering:
Prerequisite 1:
Summer 2 Offering:
Prerequisite 2:
Cross-Listed Course 1:
Prerequisite 3:
Cross-Listed Course 2:
Prerequisite 4:
Cross-Listed Course 3:
Prerequisite 5:
Cross-Listed Course 4:
Prerequisite Remarks:
Bioinformatics students only.
Cross-Listed Course 5:
Repeatable:
N
