Computational Genomics 02-710/MSCBIO2070 (co-listed as 10-810, 03-715), Spring 2007 Eric Xing, Ziv Bar-Joseph, Takis Benos, School of Computer Science, Carnegie-Mellon University

---

Announcements

Syllabus and course schedule

• 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.

People

Instructors:

• Eric Xing, Wean Hall 4127, x8-2559, Office hours: TBA
  

• Ziv Bar-Joseph, Wean Hall 4107, x8-8595, Office hours: TBA
• Takis Benos, 3078 BST-3, 648-3315, Office hours: TBA

Class Assistant:

• Monica Hopes, Wean Hall 4616, x8-5527
  

Course Description

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.

Page links

• Homework assignments

• Grading
• Class participation and reading
• Homework resources and collaboration policy
• Late homework policy
• Homework regrades policy
• Final project
• Note to people outside CMU.

Web pages for earlier versions of this course:  (include examples of midterms, homework questions, ...)

• The Spring 2005 Computational Genomics Web Page
• The Spring 2006 Computational Genomics Web Page