Meetings

           University of Colorado at Denver and UC Health Sciences Center
                        Center for Computational Biology

                              Workshop Announcement

                   To register and obtain more information, visit
http://www.cudenver.edu/ccb/workshops.html

           Title: Bioinformatics: Inference in High-throughput Molecular Biology
       Organizer: Imran Shah, UCHSC Dept. Preventive Medicine & Biometrics /
                                    Dept. Pharmacology
            Date: February 22, 2002
        Location: Room 480, CU Building, 1250 Fourteenth St., Denver
      Objectives: Build collaboration prospects between researchers in
                  molecular biology and medicine with computer scientists,
                  statisticians, probabilists and
 Target audience: Computer scientists, statisticians, probabilists and
                  computationally inclined scientists who want to learn
                  the challenging problems in molecular biology with
                  medical applications (no background in  molecular
                  biology is assumed)

 Biomedicine is entering a new era of high-throughput data production.
 The macromolecular sequence databases are doubling in size every 18
 months or so, and now contain more than 7 million sequences
 representing more than 9 billion nucleotides.  Gene chips allow for the
 simultaneous assaying of the expression levels of thousands of genes at
 a time.  Related technologies allow the identification of millions of
 point mutations in particular individuals, the simultaneous screening
 of tens of thousands of compounds for drug-like binding affinity, and
 the relatively rapid determination of three dimensional macromolecular
 structures.  The challenges inherent in analyzing this onslaught of
 extraordinarily interesting data are defining the new field of
 bioinformatics.  Making biologically relevant inferences from massive
 data sets is a key aspect of the field, drawing on techniques from
 statistics, stochastic processes, pattern recognition, and machine
 learning.  In this overview, several representative problems and
 solution techniques will be presented, including hidden Markov models,
 support vector machines, Bayesian networks, and pathway inference.

Thanks,
Harvey J. Greenberg
Director, Center for Computational Biology @ http://www.cudenver.edu/ccb/
Mathematics Dept -- Campus box 170
University of Colorado at Denver
PO Box 173364
Denver, CO 80217-3364
phone: 303-556-8464    fax: 303-556-8550
Harvey.Greenberg@cudenver.edu http://www.cudenver.edu/~hgreenbe/