WORKSHOP CO-CHAIRS:
 
  • Mohammed J. Zaki

    • Rensselaer Polytechnic Institute (zaki.AT.cs.rpi.edu )
     
  • Hannu T.T. Toivonen 

    • University of Helsinki and Nokia Research Center (Hannu.TT.Toivonen@nokia.com) 
     
  • Jason T. L. Wang

    • New Jersey Institute of Technology (jason@cis.njit.edu) 

    PROGRAM COMMITTEE:

  • Chuck Baldwin, Lawrence Livermore National Laboratory
  • Chris Bystroff, Rensselaer Polytechnic Institute
  • Shi-Kuo Chang, University of Pittsburgh
  • Wesley W. Chu, University of California, Los Angeles
  • Diane J. Cook, University of Texas at Arlington
  • Charles Elkan, University of California, San Diego
  • Janice Glasgow, Queen's University, Canada
  • Richard Hughey, University of California, Santa Cruz
  • Hasan Jamil, Mississippi State University
  • Minoru Kanehisa, Kyoto University
  • Simon M. Lin, Duke University Medical Center
  • Jacob V. Maizel, Jr., National Institutes of Health
  • Sharad Mehrotra, University of California at Irvine
  • Shinichi Morishita, University of Tokyo
  • Jane Richardson, Duke University
  • Isidore Rigoutsos, IBM Thomas J. Watson Research Center
  • Bruce Shapiro, National Institutes of Health
  • Vassilis J. Tsotras, University of California, Riverside
  • Alex Tuzhilin, New York University/Stern School of Business
  • Jeff Vitter, Duke University
  • Cathy H. Wu, Georgetown University Medical Center
  • Michael Zucker, Rensselaer Polytechnic Institute

    • BIOKDD, 2001

    Workshop on Data Mining in Bioinformatics

    in conjunction with

    7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    August 26-29, 2001
    San Francisco, CA, USA
    (KDD'2001)

    Bioinformatics is the science of storing, extracting, organizing, analyzing, interpreting, and utilizing information from biological sequences and molecules. It has been mainly fueled by advances in DNA sequencing and mapping techniques. The Human Genome Project has resulted in an exponentially growing database of genetic sequences. Knowledge Discovery and Data mining (KDD) techniques will play an increasingly important role in the analysis and discovery of sequence, structure and functional patterns or models from large sequence databases. High performance techniques are also becoming central to this task.

    Bioinformatics provides opportunities for developing novel mining methods. Some of the grand challenges in bioinformatics include protein structure prediction, homology search, multiple alignment and phylogeny construction, genomic sequence analysis, gene finding and gene mapping, as well as applications in gene expression data analysis, drug discovery in pharmaceutical industry, etc. In protein structure prediction, one is interested in determining the secondary, tertiary and quaternary structure of proteins, given their amino acid sequence. Homology search aims at detecting increasingly distant homologues, i.e., proteins related by evolution from a common ancestor. Multiple alignment and phylogenetic tree construction are inter-related problems. Multiple alignment aims at aligning a whole set of sequences to determine which subsequences are conserved. This works best when a phylogenetic tree of related proteins is available. Gene finding aims at locating the genes in a DNA sequence. Finally, in gene mapping the task is to identify potential gene loci for a particular disease, typically based on genetic marker data from patients and controls. 

    WORKSHOP TOPICS

    We solicit papers with important new insights and experiences on knowledge discovery and data mining from the modeling and simulation of complex biological systems. Topics of interest lie at the intersection of KDD and Bioinformatics. They include, but are not limited to, the following: 

    Knowledge discovery and data mining:

  • New Mining Algorithms 
  • Knowledge Representation 
  • Database Support 
  • Data Preprocessing and Cleaning 
  • Feature Selection, Analysis and Visualization 
  • Machine Learning and Pattern Recognition 
  • Neural, Rough, Fuzzy and Hybrid Techniques 
  • Hidden Markov Models 
  • Bayesian Approaches 
  • High Performance Computing 

    • Bioinformatics: 

  • Molecular Sequence Analysis 
  • Recognition of Genes and Regulatory Elements 
  • Protein Structure Prediction 
  • Interpretation of Large-Scale Gene Expression Data 
  • Gene Mapping 
  • Whole Genome Comparative Analysis 
  • Modeling of Biochemical Pathways 
  • Drug Design and Combinatorial Libraries 

    •  
    Special Issue: Authors submitting papers to this workshop are also encouraged to submit papers for an independent review and possible publication in the forthcoming special issue on Bioinformatics and Biological Data Management of Information Systems.
       
    		 

    Important Dates

    May 15, 2001: Submissions Due 
    June 15, 2001: Acceptance Notification 
    July 16, 2001:Camera Ready Copy Due 
    August 26,2001: Workshop Day

    Paper Format

    Submissions on the above and related topics of bioinformatics and data mining are invited. We also encourage submissions, which present early stages of research work, software applications and solutions. Papers should not be more than 10 pages in 10 point font and single-spaced, with one-inch margins on all sides Contact author and email address should be specified on the title page.

     

    Electronic Submission

    Electronic submission either in  PDF or PS format are strongly encouraged. 

    Please e-mail electronic submissions with subject "BIOKDD2001" to: 

    zaki.AT.cs.rpi.edu

    Hard Copy Submission

    If electronic submission is not possible send 5 hardcopies to: 
    Mohammed J. Zaki
    Department of Computer Science 
    Rensselaer Polytechnic Institute
    Troy NY 12180
    USA
    Maintained by: Mohammed J. Zaki <zaki@cs.rpi.edu>
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