CSCE555 Spring 2008 Syllabus

Department of Computer Science & Engineering, University of South Carolina Meeting Time(s): MW 4:00PM- 5:15PM, SWGN 2A21 Instructor: Dr. Jianjun Hu

One of the most important scientific events of 21st century is the Human Genome Project, which brings the complete sequence of human being into hands of anybody who has a computer. In this course, we will introduce principles and algorithms from statistics, machine learning, and pattern recognition to address exciting biological problems such as gene discovery, gene function prediction, gene expression regulation, diagnosis of cancers, etc. This course will take a case-study approach to current topics in bioinformatics. A series of projects emphasize real-life data, hands-on analysis, and collaboration. Course projects involve some programming.

Prerequisite: basic skills of programming(C/C++/Java programming/matlab/R/Perl)

Keywords: Genes, Genome sequences, gene expression regulation, machine learning, pattern recognition, disease diagnosis

Textbooks:

Introduction to Computational Genomics: A Case Studies Approach.

Essential Bioinformatics by Jin Xiong

Assignments: we will have 4-5 assignments as exercises

Project: one final project allowing u to do exicting research

Grading: assignments(40%), midterm exam(15%), project(35%), presentation(5%), attendance(5%)

Tentative Topics:

" Introduction to DNA, RNA, proteins and central dogma of molecular biology

" Sequence alignment algorithms and sequence database retrieval

" Gene finding: how to find a disease gene?

" Gene/protein function prediction: who does what?

" Hidden Markov Models for sequence pattern recognition: math talks

" DNA regulatory motif analysis: How the billions of cells are programmed to work?

" Phylogenetic analysis: trace the history of human being

" Whole genome comparison: How different between you and chimpanzee?

" Introduction to Structural bioinformatics: protein folding

" Microarray based Gene expression analysis: the real story of cells

" Case studies: find the bad genes, predict the diseases