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).
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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Document last modified on August 25, 2006.