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GS01 1133 Statistical Methods in Bioinformatics

  • Course Director(s): Yin Liu
  • Semester: Fall
  • Frequency: Annually
  • Credit Hours: 3
  • Grading System: Letter Grade
  • Prerequisites: Introduction to Mathematical Statistics (GS01 1113) or Consent of Instructor


The objective of this course is to introduce students to the concepts and statistical methods for analyzing large-scale biological data generated from emerging genomic and proteomic techniques. The course will focus on the integration of two disciplines - biology and statistics - by first describing statistical methods most often used in the field of bioinformatics and then discussing their applications on the computational analysis of gene sequence, expression and biological interactions at a large scale. The statistical methods covered include dynamic programming, maximum likelihood estimation, Bayesian inference, Hidden Markov Models, Markov chain Monte Carlo, classification and clustering methods. The students will master advanced applications of statistical computing in a wide range of biological and biomedical problems, including multiple sequence alignment, biomarker and disease gene identification, inference of protein interaction network, functional modules and signal transduction networks.