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

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).

Slides:

• Philip E. Bourne, University of California San Diego
  Machine Learning as Applied to Structural Bioinformatics:
  Results and Challenges

• Liran Carmel, Igor B. Rogozin, Yuri I. Wolf and Eugene V. Koonin, NCBI, NLM, National Institutes of Health
  An EM Algorithm for Inferring the Evolution of Eukaryotic Gene Structure

• James Howse, Los Alamos National Laboratory
  A machine learning approach for predicting the EC numbers of proteins

• Sungchul Ji, Rutgers University
  How to Avoid Misinterpreting Microarray Data

• Christina Leslie, Columbia University
  Learning the cis regulatory code by predictive modeling
  of gene regulation

• Zhenqiu Liu, University of Maryland Medicine
  Genome-wide SNP Selection with Entropy Based Methods

• Feng Luo, Clemson University
  Modular Organization of Protein Interaction Network

• Chandan Reddy, Cornell University
  Motif Refinement using Hybird Expectation Maximization Algorithm

• Aik Choon Tan, Johns Hopkins University
  Simple Decision Rules for Classifying Human Cancers from Gene Expression Profiles

• Anne Zhang, The University of Kansas
  Cancer Classification with Data-dependent Kernels

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