Research methods in Bioinformatics

MATH4875, STAT3980, STAT7030 (Department of Mathematics, Hong Kong Baptist University)

Topics

I. Introduction to Bioinformatics
- A. Big data in statistical genomics [Slides][Suggested Reading: Mining the Big Data Mountain; The Rising of Big Data] [Is Most of Our DNA Garbage?]
- B. Example: Genome-wide association studies (GWAS) [Slides]
II. Statistical models for large-scale problems in Bioinformatics
- A. James-Stein estimator [Slides] [The Preface of "Lady Tasting Tea"]
- B. Mixture models and false discovery rates [Slides] [More materials from Prof. Efron's Book "Large-Scale Inference"]
- C. Linear mixed models [Slides]
- D. Convex statistical models [Slides] [Convex optimization by Prof. Boyd & Prof. Vandenberghe]
III. Algorithms for large-scale problems in Bioinformatics
- A. EM algorithms and extension [Slides]
- B. Some recent developed algorithms for large-scale problems in Bioinformatics. [Slides] [More materials on gradient methods]
IV. Integrative analysis in Bioinformatics
- A. Case Study: Integrative analysis for Genome-wide association studies. [Slides][Supporting information: Software and data]
- B. Case Study: Low-rank approximation and its applications in Bioinformatics. [Slides][Matlab Demo]

Mining the Big Data Mountain (TED talk by Prof. Butte at Stanford)

Homework

Memo

Fall 2014. Mr. Haoyu Liu, department of mathematics, HKBU, was ranked the FIRST for the real data prediction problem.

References