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Date: Tue, 05 Nov 1996 00:26:13 GMTServer: NCSA/1.5Content-type: text/htmlLast-modified: Fri, 08 Sep 1995 19:34:50 GMTContent-length: 7381<HTML><HEAD><TITLE> Computational Biology in the UW-Madison CS Dept.</TITLE></HEAD><BODY><H1> <!WA0><IMG ALIGN=MIDDLE SRC="http://www.cs.wisc.edu/~shavlik//~shavlik/images/DNA-horiz.gif"> </H1><H1> Computational Biology in the UW-Madison CS Dept</H1><!WA1><img src="http://www.cs.wisc.edu/~shavlik//~shavlik/images/rainline.gif"><P>As a young science, computational biology offers a wealth of researchopportunities. At the University of Wisconsin-Madison, scientists fromchemistry, computer science, genetics, mathematics, molecular biology, plantpathology, and other disciplines are applying computational methods to variousbiological problems. Some investigations in the Department of ComputerSciences involve DNA sequencing and analysis, experiment management, modellingof ecological communities, protein-folding prediction, and speciesidentification. Cross-disciplinary training programs are available forgraduate students interested in careers in computational biology.<P>Research groups investigating computational problems in biology are in threesubfields of computer science: artificial intelligence, databases, and theory.Each group collaborates with various biological laboratories on campus. Asummary of some of the research activities follows.<P>The artificial intelligence group working with Professor <!WA2><A HREF="http://www.cs.wisc.edu/~shavlik//~shavlik/shavlik.html">Jude Shavlik</A> appliesmachine learning techniques to several problems in molecular biology.Problems under investigation include: predicting protein secondary-structure;distinguishing protein-coding and noncoding regions; and recognizingpromoters, splice junctions, terminators, introns, and ribosome-binding sites.Machine learning methods aid in the discovery of concepts underlying phenomenathrough the examination of multiple examples. For instance, a system can learnto find genes by examining many DNA sequences, each classified as to whetheror not it contains a gene. This technique is powerful and potentially veryvaluable to the biological community. Currently, the primary research focusinvolves the incorporation of existing biological knowledge with computationaldiscovery methods.<P>The database group headed by Professors <!WA3><A HREF="http://www.cs.wisc.edu/~shavlik//~yannis/yannis.html">Yannis Ioannidis</A> and <!WA4><A HREF="http://www.cs.wisc.edu/~shavlik//~pubs/faculty-info/livny.html">Miron Livny</A> isdeveloping a desktop experiment management system that will assist scientistsin managing their experimental studies. The goal of the system is to have asingle tool controlling the experimentation processes, managing the generateddata, and efficiently processing user requests for data. An object-orienteddatabase system is at the core of the system under development. This projectproceeds in collaboration with several laboratories on campus, primarily thosein the Departments of Soil Sciences, Molecular Biology, and Genetics. Thesegroups are involved in simulation-based modelling of plant growth, microscopicimaging, and DNA sequencing, respectively.<P>The research group led by Professor <!WA5><A HREF="http://www.cs.wisc.edu/~shavlik//~pubs/faculty-info/joseph.html">Deborah Joseph</A> applies techniques fromtheoretical computer science to develop algorithms for computational biologyapplications. One research project in collaboration with the WisconsinE. coli Genome Project is leading to computational methods that generateaccurate alignments of overlapping DNA sequences. Other work analyzessequence data for interesting biological features. For instance, incollaboration with a group in plant pathology, one class of DNA sequence isbeing used to develop quantitative methods for identifying possible biologicalcontrol organisms in ecological communities. Some of the algorithms developedby this project have been implemented on the department's parallel computers.<P>Two training programs offer cross-disciplinary training for doctoral studentsearly in their graduate programs. The NIH-funded Biotechnology TrainingProgram was established to train scientists and engineers to effectively applyinterdisciplinary research tools to solve problems of biotechnologicalsignificance. This program has students throughout the biological andphysical sciences involved in research problems of specific biotechnologicalrelevance. The Applied Mathematics Training Program is being established totrain scientists to effectively apply mathematical and computational tools toa wide range of scientific endeavors. Although specific to mathematicalapplications, students in this program can address a broad range of researchproblems including many in the biological sciences. Traineeships in bothprograms can be awarded to students entering graduate school.<P><!WA6><img src="http://www.cs.wisc.edu/~shavlik//~shavlik/images/rainline.gif"><P><H3> Training Programs </H3> <UL> <LI> <!WA7><A HREF="http://www.cs.wisc.edu/~shavlik//~shavlik/apply-math.html">Applied Mathematics Training Program</A> <LI> <!WA8><A HREF="http://www.cs.wisc.edu/~shavlik//~shavlik/biotech.html">Biotechnology Training Program</A></UL><P>Amy Kryder (kryder@cs.wisc.edu) and <!WA9><A HREF="http://www.cs.wisc.edu/~shavlik//~allex/allex.html">Carolyn Allex</A> (allex@cs.wisc.edu)are current holders of Biotechnology Training fellowships; feel freeto contact them with questions about the program.<H3> <!WA10><A HREF="gopher://fyvie.cs.wisc.edu/11/uwcs/grad/"> Graduate Study in Computer Sciences</A> </H3><H3> Electronic Access to Wisconsin Papers </H3> <P>The <!WA11><A HREF="http://www.cs.wisc.edu/">Wisconsin CS department</A>maintains an <!WA12><A HREF="ftp://ftp.cs.wisc.edu">electronic (ftp) archive</A> of technical reports, other papers, and software. <P> The subdirectory <!WA13><A HREF="ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/">machine-learning/shavlik-group</A> contains additional papers byShavlik's research group. See the file <!WA14><A HREF="ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/abstracts">abstracts</A> for a list of papers, which are in compressedpostscript. (The papers in the files <!WA15><A HREF="ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/shavlik.tr92.ps">shavlik.tr92.ps</A> and <!WA16><A HREF="ftp://ftp.cs.wisc.edu/machine-learning/shavlik-group/craven.mlrgwp93.ps">craven.mlrgwp93.ps</A> are recommended as the first ones to read.)<H3> Some Interesting Links </H3> <UL> <LI> Local Links <UL> <LI> <!WA17><A HREF="http://www.cs.wisc.edu/~shavlik//~shavlik/uwai.html"> U-Wisc AI Group Home Page</A> <LI> <!WA18><A HREF="http://www.cs.wisc.edu/~shavlik//~hellers/dbmshome.html"> U-Wisc DB Group Home Page</A> <LI> <!WA19><A HREF="http://www.cs.wisc.edu/"> U-Wisc CS Dept Home Page</A> <LI> <!WA20><A HREF="gopher://gopher.cs.wisc.edu"> U-Wisc CS Gopher</A> <LI> <!WA21><A HREF="gopher://cms.wisc.edu"> U-Wisc Center for Mathematical Sciences Gopher</A> </UL> <LI> External Compbio-Related Links <UL> <LI> <!WA22><A HREF="http://golgi.harvard.edu/biopages.html"> Info on Biosciences</A> <LI> <!WA23><A HREF="http://www.gdb.org/hopkins.html"> Johns Hopkins Bio-Informatics Home Page</A> <LI> <!WA24><A HREF="http://ibc.wustl.edu/compbio"> Wash. U. in St. Louis Inst. for Biocomputing</A> <LI> <!WA25><A HREF="http://www.msrc.pnl.gov:2080/docs/cie/neural/neural.homepage.html"> Info and Refs on Neural Nets and Molecular Science</A> <LI> <!WA26><A HREF="http://www.ncbi.nlm.nih.gov/"> Genbank</A> </UL> </UL><HR><ADDRESS> Last Changed: February 22, 1995 by shavlik@cs.wisc.edu </ADDRESS><HR></BODY></HTML>⌨️ 快捷键说明
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