课程名称 (Course Name) ： Bioinformatics

课程代码 (Course Code）： X033526

学分/学时 (Credits/Credit Hours)： 3

开课时间 (Course Term )：  Spring

开课学院（School Providing the Course）:  Department of Computer Science

任课教师（Teacher）:  Bo Yuan, Ph.D., Professor of Computer Science & Engineering

课程讨论时数（Course Discussion Hours）:  

课程实验数（Lab Hours）:   

课程内容简介(Course Introduction):

This course in computational biology is designed to introduce the topic, history, and current research issues in bioinformatics, as well as prepare students in the skills necessary to communicate with researchers across the disciplines of computer science and biology. The course is designed for computer scientists who want an introduction to the language of molecular biology and to the significant computational problems in the field of biology. The course is also designed for biophysicists, biochemists, and molecular biologists who want an introduction to the language of computer programming and algorithm development, focusing on solving computational problems in biology. As part of the course, computer scientists and bio-scientists will work in groups to design, build, implement, and test software packages to solve particular computational problems in biology.

教学大纲(Course Teaching Outline):

1.       Introduction
Course Introduction, Review of Modern Biology I
Abstraction Level 1: Sequence and the Human Genome
Introduction to Bioinformatics Laboratory / Bioinformatics in the Computer Science\

2.       Abstraction Level 1: Sequence
Review of Modern Biology II
Sequence Analysis: Motif and Regulation

3.       Abstraction Level 1: Sequence
Sequence Analysis: Genes and Genome
Sequence Analysis: Gene Evolution

4.       Abstraction Level 2: Expression
Microarray Expression Data Analysis
Machine Learning: Bayesian Methodologies

5.       Abstraction Level 2: Expression
Gene Regulatory Networks
Abstraction Level 4: Systems/Misc
Graphical Model, and Conditional Dependence of Gene Expressions in Biological Systems

6.       Abstraction Level 4: Systems/Misc
Scale-free Networks I
Scale-free Networks II

7.       Abstraction Level 3: Proteomics
Statistical Models and Stochastic Processes in Proteomics
Signal Processing for Proteomics

8.       Abstraction Level 3: Proteomics
3D protein structure and Prediction (I)
Homology modeling

9.       Abstraction Level 3: Proteomics
3D protein structure and Prediction (II)
Protein threading

10.   Integration: Systems Biology

Biological Diversity and Model Searching Programs

Graphical model, regularization and gene-gene interactions (revisited)

课程进度计划(Course Schedule):

1.       Introduction
Course Introduction, Review of Modern Biology I
Abstraction Level 1: Sequence and the Human Genome
Introduction to Bioinformatics Laboratory / Bioinformatics in the Computer Science\

2.       Abstraction Level 1: Sequence
Review of Modern Biology II
Sequence Analysis: Motif and Regulation

3.       Abstraction Level 1: Sequence
Sequence Analysis: Genes and Genome
Sequence Analysis: Gene Evolution

4.       Abstraction Level 2: Expression
Microarray Expression Data Analysis
Machine Learning: Bayesian Methodologies

5.       Abstraction Level 2: Expression
Gene Regulatory Networks
Abstraction Level 4: Systems/Misc
Graphical Model, and Conditional Dependence of Gene Expressions in Biological Systems

6.       Abstraction Level 4: Systems/Misc
Scale-free Networks I
Scale-free Networks II

7.       Abstraction Level 3: Proteomics
Statistical Models and Stochastic Processes in Proteomics
Signal Processing for Proteomics

8.       Abstraction Level 3: Proteomics
3D protein structure and Prediction (I)
Homology modeling

9.       Abstraction Level 3: Proteomics
3D protein structure and Prediction (II)
Protein threading

10.   Integration: Systems Biology

Biological Diversity and Model Searching Programs

Graphical model, regularization and gene-gene interactions (revisited)

课程考核要求(Course Assessment Requirements)：

There are four assignment reports for this course. Students may collaborate with classmates on the experiments, but copying experiment reports is not permitted. Any student that copies another experiment report or allows their experiment report to be copied will be assigned a 0 for that experiment report. Each of the reports will be 10%, plus 10% class participation.

In addition to the experiment reports, there will also be a final exam during exam week. The final exam will be comprehensive and will cover material from the entire course. The exam will be 50%.

参考文献(Course References)：

Book Chapter

Alterovitz, G., E. Afkhami, and M. Ramoni. "Robotics, Automation, and Statistical Learning for Proteomics." In Focus on Robotics and Intelligent Systems Research. Edited by F. Columbus. Vol. 1. New York: Nova Science Publishers, Inc., 2005.

Texts

Oppenheim, A. V., A. S. Willsky, and H. Nawab. Signals and Systems. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1997. ISBN: 0138147574.

Papoulis, A., and S. U. Pillai. Probability, Random Variables and Stochastic Processes: Sanitary and Water Resources Engineering (Sanitary & Water Resources Engineering S). New York, NY: McGraw-Hill, 2002. ISBN: 0072817259.

Kohane, I. S., A. T. Kho, and A. J. Butte. Microarrays for an Integrative Genomics. Cambridge, MA: MIT Press, 2002. ISBN: 026211271X.

预修课程（Prerequisite Course）