BIOENG598 = STAT578

Models and Computations for Functional Genomics

Previous course webpage

Papers to be discussed

Schedule

Meeting: Spring 2006, 3pm - 5:30pm, Wednesdays, 3211 Digital Computer Lab.
Credits : 3 graduate hours.

Course Reference number: 46468 

Instructor: Sheng Zhong (szhong AT uiuc DOT edu)
Enrollment Limited to: 10 graduate students. NO audit.

Content

This course is design to:

·        Introduce the modern modeling and computational techniques in the analysis of genomics data.

·        Help graduate students to develop the research capability in bioinformatics.

·        Help graduate students to develop the capability to identify research directions with applicable biomedical values.

Computational topics include:

·        Linear and generalized linear models

·        Novel machine learning techniques

·        Multiple hypothesis testing issues and Estimation of False Discovery Rate

·        Data mining for signaling pathway and Gene Ontology information

·        Bayesian methods

·        Monte Carlo computation

·        (tentative/ time permit) Comparative genomics

Biological topics include:

·        Microarray / comparative sequence analysis for stem cell differentiation (background)

·        Microarray / comparative sequence analysis for segmentation clock (background)

·        Cancer genetics (background)

Prerequisites

·        Proficient with any one of the following languages: R, C++, C#, Perl, Python, Matlab (Only one, NOT all!)

 

·        Good understanding of probability and statistics

Format

Course meeting time is used for instructor’s lectures, software demonstration, student paper presentation, student project proposal and student project report/presentation. Substantial after lecture reading will be assigned. No traditional homework. No exams. A course project is mandated.

Textbooks

No textbook is required. For potential interest in systematic study, the following books are suggested.

·         For microarray analysis:
The Analysis of Gene Expression Data. Giovanni Parmigiani, Elizabeth S. Garett, Rafael A. Irizarry, Scott L. Zeger. Amazon

 

·         For DNA/protein sequence analysis:
Biological sequence analysis: Probablistic Models of Proteins and Nucleic Acids. Durbin, Eddy, Krogh, Mitchison. Amazon

 

·         For machine learning:
The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman. Amazon
 

Grading