INFO-B 429 Machine Learning for Bioinformatics
Prerequisites: INFO-I 223, PBHL-B 302, and BIOL-K 101
This course covers machine learning theories and methods and their
application to biological sequence analysis, gene expression data analysis,
genomics and proteomics data analysis, and other problems in bioinformatics.
- Access public-domain biological datasets.
- Analyze genomics and proteomics data using decision theories, decision trees, and random forests.
- Analyze gene expression data using linear classification, logistic regression, SVM, clustering, and biclustering.
- Analyze biological sequence data using expectation-maximization methods and hidden Markov models.
- Analyze and visualize biological data sets using R packages for machine learning.
- Design computational experiments for training and evaluating machine learning methods for solving bioinformatics problems.