DIMACS Workshop on Machine Learning Techniques in Bioinformatics

July 11 - 12, 2006
DIMACS Center, CoRE Building, Rutgers University

Organizers:
Dechang Chen, Uniformed Services University of the Health Services, dchen@usuhs.mil
Xue-Wen Chen, University of Kansas, xwchen@ku.edu
Sorin Draghici, Wayne State University, sod@cs.wayne.edu
Presented under the auspices of the DIMACS/BioMaPS/MB Center Special Focus on Information Processing in Biology.

This special focus is jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), the Biological, Mathematical, and Physical Sciences Interfaces Institute for Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and Biophysical Chemistry (MB Center).

Workshop Program:

Tuesday, July 11, 2006

 8:15 -  9:15 	Breakfast and Registration 

 9:15 -  9:30 	Welcome and Opening Remarks
              	Mel Janowitz, DIMACS Associate Director

 9:30 - 10:15 	Predicting Protein Structure Flexibility from Sequence
		Philip E. Bourne, University of California San Diego

10:15 - 11:00 	Break 

11:00 - 11:45   Cancer Tissue Classification with Data-dependent Kernels
		Anne Zhang, The University of Kansas

12:00 -  1:30 	Lunch

 1:30 -  2:15 	Modular Organization of Protein Interaction Network
 		Feng Luo, Clemson University

 2:15 -  3:00 	Comparing the Performance of Several Popular Machine Learning 
                Algorithms on Classifying TATA-box from putative TATA boxes
		Raja Loganantharaj, University of Louisiana at Lafayette

 3:00 -  3:30 	Break

 3:30 -  4:15 	Simple decision rules for classifying human cancers from 
                gene expression profiles
		Aik Choon Tan, Johns Hopkins University

 4:15 -  5:00 	A machine learning approach for predicting the EC 
                numbers of proteins
		James Howse, Los Alamos National Laboratory

 5:30         	Dinner at DIMACS


Wednesday, July 12, 2006

 8:15 -  9:00 	Breakfast and Registration

 9:00 -  9:45 	Motif Refinement by Improving Information Content
                Scores using Neighborhood Search
		Chandan Reddy, Cornell University

 9:45 - 10:30 	An expectation-maximization algorithm for inferring the 
                evolution of eukaryotic gene structure
		Liran Carmel, National Institutes of Health

10:30 - 11:00 	Break

11:00 - 11:45 	Learning the cis regulatory code by predictive modeling 
                of gene regulation
		Christina Leslie, Columbia University

12:00 - 1:30  	Lunch

 1:30 -  2:15 	Genome-wide Tagging SNPs with Entropy Based Methods
		Zhenqiu Liu, University of Maryland Medicine

 2:15 -  3:00 	Machine Learning and data combination for regulatory
                pathway prediction
		Mark Kon, Boston University

 3:00 -  3:45 	How to Avoid Misinterpreting Microarray Data
 		Sungchul Ji, Rutgers University
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Document last modified on May 11, 2006.