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 CSE 590 CB, Spring 2001
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Reading and Research in Computational Biology
Fridays, 3:30 - 5:00, MGH 251

CSE 590 CB is a weekly seminar on Readings and Research in Computational Biology, open to all graduate students in the computer, biological, and mathematical sciences.
Organizers:  Larry Ruzzo, Rimli Sengupta
Credit: 1-3 Variable
Grading: Credit/No Credit. Talk to the organizers if you are unsure of our expectations.
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 Schedule
Date Topic Presenters/Participants Papers
03/30 Organizational Meeting    
04/06 Exploring cancer and cancer therapies through agent-based modeling. Guest Speaker: Carlo Maley, FHCRC Abstract
04/13 Feature Selection: FS 101 Don, Ken  
04/20 Feature Selection: 2 nonparametric methods Gidon, Saurabh 1 ,2
04/27 Feature Selection: a survey Jochen 3
05/04 Feature Selection: SVMs Brian, Mathieu 4, 5
05/11 Array Analysis: More SVMs Ka Yee 10
05/18 Array Analysis: A Promoter Model Dan 7
05/25 Computational issues in protein folding, structure prediction, and design Guest Speaker: David Baker, Biochem  
06/01 Array Analysis: Bayes Nets Emily, Tammy 8, 9

 Papers, etc.

4/6:   Exploring cancer and cancer therapies through agent-based modeling.

Dr. Carlo Maley, Human Biology Division, Fred Hutchinson Cancer Research Center

Abstract: Curing cancer has proven to be an extremely hard problem. The US government has invested billions of dollars in cancer research over the last half century and still Americans have about a 40% chance of developing cancer in their lifetime. Why is it such a hard problem? Because the cells in a tumor evolve. Like HIV, tumor cells mutate rapidly and quickly develop resistance to any therapies that we apply. Until recently, it has been difficult to study the evolutionary dynamics of cancer, let alone counter them. This is beginning to change. Today we have the technology to examine mutant cells in detail. We are also developing the theoretical tools necessary to understand these complex dynamics. I will introduce a set of agent-based (a.k.a., "configuration" or "individual-based") models that simulate both the evolutionary dynamics of cancer, as well as the effects of some hypothetical therapies. I will introduce these models as well as some of the insights we have gained through their use.

Topic I: Classification and Feature Selection with Microarray Data

There is a rapidly growing literature on use of microarray data to discriminate between, say, tumor and normal samples, and to identify those genes that seem most strongly linked to such discriminations. We're going to tackle some or all of the following papers.

It would be great if any of you wanted to experiment with any of these or other methods on some real data. Some of the data sets that have been commonly used in these studies are the following. Please let us know if you (a) find other data, and/or (b) learn anything interesting from looking at it.

Data Sets:

References:

  1. Ben-dor et al. "Tissue classification with gene expression profiles", JCB 2000. Paper [The following is a related TR: "Scoring genes for relevance". Paper]
  2. Park, Pagano, Bonetti, "A nonparametric scoring algorithm for identifying informative genes from microarray data", PSB 2001. Paper
  3. Claverie, "Computational methods for identification of differential and coordinated gene expression", Human Molecular Genetics 1999. (pdf)
  4. Guyon, Weston, Barnhill, Vapnik, "Gene selection for cancer classification using support vector machines", Machine Learning 2000. (pdf)
  5. Pavlidis, Weston, Cai, Grundy "Gene functional classification from heterogenous data", Recomb 2001. Paper
  6. Link to feature selection literature (for background/review). There's a ton of stuff here. To be used as a resource.

Topic 2: (Non-Clustering) Analysis of Microarray Data

We're going to tackle some or all of the following papers.

References:

  1. Bussemaker, Li, and Siggia, "Regulatory element detection using correlation with expression." Nature Genetics, 27 (Feb 2001) 167-171. (pdf,   Siggia's talk, from MSRI)
  2. Friedman, Linial, Nachman, and Pe'er, "Using Bayesian networks to analyze expression data." J. Computational Biology 7:601-620, 2000. Paper.
  3. A.J. Hartemink, D.K. Gifford, T.S. Jaakkola, and R.A. Young, "Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks." Pacific Symposium on Biocomputing 6:422-433 (2001). Paper.
  4. Brown, MPS, WN Grundy, D Lin, N Cristianini, C Sugnet, TS Furey, M Ares, Jr., and D Haussler. "Knowledge-based analysis of microarray gene expression data by using support vector machines." Proceedings of the National Academy of Science. 97(1):262-267, 2000. Paper.
  5. Pe'er, Regev, Elidan, and Friedman. "Inferring Subnetworks from Perturbed Expression Profiles." Bioinformatics, 2001. Paper.
  6. And three good web tutorials/resources on Bayes nets that Tammy found:

 Other  Seminars Applied Math Department Mathematical Biology Journal Club
Biochemistry Department Seminars
COMBI Seminars (MBT 599C)
Genetics Department Seminars
Microbiology Department Seminars
Molecular Biotechnology Department Seminars
Zoology 525, Mathematical Biology Seminar Series

 Resources MBT 599 (aka MBT/GENET 540/541) Introduction to Computational Molecular Biology: Genome and Protein Sequence Analysis (Winter/Spring 2001)
CSE 590 CB, Winter, 2001.
CSE 590 CB, Autumn, 2000.
CSE 590 CB, Spring, 2000.
CSE 590 CB, Winter, 2000.
CSE 590 CB, Autumn, 1999.
CSE 590 CB, Spring, 1999.
CSE 590 CB, Winter, 1999.
CSE 590 CB, Autumn, 1998.
Lecture notes from CSE 527 (Computational Biology) (formerly known as CSE 590 BI).


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