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 CSE 590 CB, Au '04: Reading & Research in Comp. Bio.
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 Course Info    CSE 590CB is a weekly seminar on Readings and Research in Computational Biology, open to all graduate students in computational, biological, and mathematical sciences.
When/Where:  Mondays, 3:30 - 4:50, MEB 243
Organizers:  Joe Felsenstein, Michal Linial, Bill Noble, Emily Rocke, Larry Ruzzo, Martin Tompa
Credit: 1-3 Variable
Grading: Credit/No Credit. Talk to the organizers if you are unsure of our expectations.
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 Date  Presenters/Participants Topic Papers
10/04   ---- Organizational Meeting ----  
10/11 Ka Yee Yeung, Microbiology and CEA Gene selection using Bayesian Model Averaging: application to multi-class classification of microarray data Abstract
10/18 Jonathon, Travis (Emily) Position-based Motif Discovery Paper
10/25 Alex, Ross (Martin) Mammal Sequencing: Who's next? Paper
11/01 Brig, Zasha (Larry) Finding ncRNA Genes Paper
11/08 Ora Furman, Biochemistry Docking  
11/15 Divya, Kasia, Nan, Rosalia (Michal) Detecting Signatures in Proteins Papers
11/22 Divya, Kasia, Nan, Rosalia (Michal) Integration of Biological Data Papers
11/29 Greg, Lisa, Zizhen (Joe) Expression to Phylogeny Papers
12/06 Amol, Becky (Bill) Quality Assessment of Tandem Mass Spectra Papers

 Papers, etc.

  Note on Electronic Access to Journals

Links to full papers below are often to journals that require a paid subscription. The UW Library is generally a paid subscriber, and you can freely access these articles if you do so from an on-campus computer. For off-campus access, look at the library "proxy server" instructions.

During Autumn 2004, the Library is deploying a new proxy server; if you are accessing this page from an offcampus computer, the "[offcampus]" links below may be more convenient. You will be prompted for your UW net ID and password once per session.  

10/11: Gene selection using Bayesian Model Averaging: application to multi-class classification of microarray data -- Ka Yee Yeung, Microbiology and CEA

   Abstract:  Motivation: Selection of relevant genes is critical to accurately predict the class of a tissue sample from its gene expression profiles. In this talk, we present the merits of using Bayesian Model Averaging (BMA) in gene selection and classification on microarray data. Typical gene selection and classification procedures ignore model uncertainty such that one set of relevant genes (model) is chosen to predict the class. BMA accounts for the model uncertainty by averaging over multiple models (sets of potentially overlapping relevant genes). Results: We showed that BMA selects relatively small numbers of relevant genes and achieves high prediction accuracy on three microarray datasets. Our BMA algorithm is applicable to microarray datasets with any numbers of classes, outputs posterior probabilities for the selected genes and models. Our selected models are typically simple, consisting of only a few genes. By averaging over multiple simple models, our BMA algorithm achieves high prediction accuracy.

10/18: Position-based Motif Discovery -- Jonathon, Travis (Emily)

10/25: Mammal Sequencing: Who's next? -- Alex, Ross (Martin)

11/01: Finding ncRNA Genes -- Brig, Zasha (Larry)

11/08: Docking -- Ora Furman, Biochemistry

11/15: Detecting Signatures in Proteins -- Divya, Kasia, Nan, Rosalia (Michal)

11/22: Integration of Biological Data -- Divya, Kasia, Nan, Rosalia (Michal)

11/29: Expression to Phylogeny -- Greg, Lisa, Zizhen (Joe)

12/06: Quality Assessment of Tandem Mass Spectra -- Amol, Becky (Bill)

 Other  Seminars Past quarters of CSE 590CB
COMBI & Genome Sciences Seminars
Applied Math Department Mathematical Biology Journal Club
Biostatistics Seminars
Microbiology Department Seminars
Zoology 525, Mathematical Biology Seminar Series

 Resources Molecular Biology for Computer Scientists, a primer by Lawrence Hunter (46 pages)
A Quick Introduction to Elements of Biology, a primer by Alvis Brazma et al.
S-Star Bioinformatics Online Course Schedule, a collection of video primers
A very comprehensive FAQ at, including annotated references to online tutorials and lectures.
CSE 527: Computational Biology
CSE 590TV: Computational Biology (Professional Masters Program)
Genome 540/541: Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis

CSE's Computational Molecular Biology research group
Interdisciplinary Ph.D. program in Computational Molecular Biology

CSE logo Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA  98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX
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