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Computational
Genomics
02-710/MSCBIO2070
(co-listed as 10-810, 03-715),
Spring 2007
School of Computer Science, Carnegie-Mellon University
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- Starting
date: January
16, 2007
- Class lectures:
Tuesdays & Thursdays from 10:30-11:50pm
- Location: Wean 5304
- Recitation:
- Textbook (suggested but
not required):
- Durbin et al, Biological Sequence Analysis.
- Deonier,
Tavare and Waterman, Computational
Genome Analysis.
Instructors:
- Eric Xing, Wean Hall
4127, x8-2559, Office hours: TBA
Class Assistant:
Dramatic advances in experimental technology and computational analysis
are fundamentally transforming the basic nature and goal of biological
research. The emergence of new frontiers in biology, such as
evolutionary genomics and systems biology is demanding new
methodologies that can confront quantitative issues of substantial
computational and mathematical sophistication. In this course we will
discuss classical approaches and latest methodological advances in the
context of the following biological problems: 1) Computational
genomics, focusing on gene finding, motifs detection and sequence
evolution. 2) Medical and
populational genetics, focusing on polymorphism analysis, linkage
analysis, pedigree and genetic demography, 3) Analysis of high
throughput biological data, such as gene expression data, focusing on
issues ranging from data acquisition to pattern recognition and
classification. 4) Molecular and regulatory evolution, focusing on
phylogenetic inference and regulatory network evolution, and 5) Systems
biology, concerning how to combine sequence, expression and other
biological data sources to infer the structure and function of
different systems in the cell. From the computational side this course
focuses on modern machine learning methodologies for computational
problems in molecular biology and genetics, including probabilistic
modeling, inference and learning algorithms, pattern recognition, data
integration, time series analysis, active learning, etc.
Students are expected to have successfully
completed
10701 (Machine Learning), or an equivalent class.
Web pages for
earlier versions of this course: (include
examples of
midterms, homework questions, ...)