59154

神经网络昆斯林的新闻组分类2006

[4] "Toward Multi-Strategy Parallel Learning in Sequence 
 Analysis"  P.K. Chan & S.J. Stolfo

[5] "Protein Structure Prediction: Selecting Salient Features 
 from Large Candidate Pools"  K.J. Cherkauer & J.W. Shavlik

[6] "Comparison of Two Approaches to the Prediction of Protein 
 Folding Patterns"  I. Dubchak, S.R. Holbrook, & S.-H. Kim

[7] "A Modular Learning Environment for Protein Modeling"
 J. Gracy, L. Chiche & J. Sallantin

[8] "Inference of Order in Genetic Systems" 
 J.N. Guidi & T.H. Roderick

[9] "PALM - A Pattern Language for Molecular Biology"
 C. Helgesen & P.R. Sibbald

[10] "Grammatical Formalization of Metabolic Processes"  
 R. Hofestedt

[11] "Representations of Metabolic Knowledge"  
 P.D. Karp & M. Riley

[12] "Protein Sequencing Experiment Planning Using Analogy"
 B. Kettler & L. Darden

[13] "Design of an Object-Oriented Database for Reverse Genetics"  
 K.J. Kochut, J. Arnold, J.A. Miller, & W.D. Potter

[14] "A Small Automaton for Word Recognition in DNA Sequences"
 C. Lefevre & J.-E Ikeda

[15] "MultiMap:  An Expert System for Automated Genetic Linkage 
 Mapping"  T.C. Matise, M. Perlin & A. Chakravarti

[16] "Constructing a Distributed Object-Oriented System with 
Logical Constraints for Fluorescence-Activated Cell Sorting"
 T. Matsushima

[17] "Prediction of Primate Splice Junction Gene Sequences with 
 a Cooperative Knowledge Acquisition System"
 E.M. Nguifo & J. Sallantin

[18] "Object-Oriented Knowledge Bases for the Analysis of 
 Prokaryotic and Eukaryotic Genomes" 
 G. Perriere, F. Dorkeld, F. Rechenmann, & C. Gautier

[19] "Petri Net Representations in Metabolic Pathways"
 V.N. Reddy, M.L. Mavrovouniotis, & M.L. Liebman

[20] "Minimizing Complexity in Cellular Automata Models of 
 Self-Replication"  J.A. Reggia, H.-H. Chou, S.L. Armentrout, 
 & Y. Peng

[21] "Building Large Knowledge Bases in Molecular Biology"
 O. Schmeltzer, C. Medigue, P. Uvietta, F. Rechenmann, 
 F. Dorkeld, G. Perriere, & C. Gautier

[22] "A Service-Oriented Information Sources Database for the 
 Biological Sciences"  G.K. Springer & T.B. Patrick

[23] "Hidden Markov Models and Iterative Aligners: Study of their 
 Equivalence and Possibilities" H. Tanaka, K. Asai, M. Ishikawa,
 & A. Konagaya

[24] "Protein Structure Prediction System Based on Artificial 
 Neural Networks"  J. Vanhala & K. Kaski

[25] "Transmembrane Segment Prediction from Protein Sequence 
 Data"  S.M. Weiss, D.M. Cohen & N. Indurkhya

                      TUTORIAL PROGRAM
Tutorials will be conducted at the Bethesda Ramada Hotel on 
Tuesday, July 6.

12:00-2:45pm "Introduction to Molecular Biology for Computer 
 Scientists"  Prof. Mick Noordewier (Rutgers University)

This overview of the essential facts of molecular biology is 
intended as an introduction to the field for computer scientists 
who wish to apply their tools to this rich and complex domain.  
Material covered will include structural and informational 
molecules, the basic organization of the cell and of genetic 
material, the "central dogma" of gene expression, and selected 
other topics in the area of structure, function, and regulation as 
relates to current computational approaches.  Dr. Noordewier has 
appointments in both Computer Science and Biology at Rutgers, and 
has extensive experience in basic biological research in addition 
to his current work in computational biology.

12:00-2:45pm "Introduction to Artificial Intelligence for 
 Biologists"  Dr. Richard Lathrop (MIT & Arris Corp.)

An overview of the field of artificial intelligence will be 
presented, as it relates to actual and potential biological 
applications.  Fundamental techniques, symbolic programming 
languages, and notions of search will be discussed, as well as 
selected topics in somewhat greater detail, such as knowledge 
representation, inference, and machine learning.  The intended 
audience includes biologists with some computational background, 
but no extensive exposure to artificial intelligence.  Dr. 
Lathrop, co-developer of ARIADNE and related technologies, has 
worked in the area of artificial intelligence applied to 
biological problems in both academia and industry.

3:00-5:45pm "Neural Networks, Statistics, and Information Theory 
 in Biological Sequence Analysis" Dr. Alan Lapedes (Los Alamos 
 National Laboratory) 

This tutorial will cover the most rapidly-expanding facet of 
intelligent systems for molecular biology, that of machine 
learning techniques applied to sequence analysis.  Closely 
interrelated topics to be addressed include the use of artifical 
neural networks to elicit both specific signals and general 
characteristics of sequences, and the relationship of such 
approaches to statistical techniques and information-theoretic 
views of sequence data.  Dr. Lapedes, of the Theoretical 
Division at Los Alamos, has long been a leader in the use of such 
techniques in this domain.

3:00-5:45pm "Genetic Algorithms and Genetic Programming" 
 Prof. John Koza (Stanford University)

The genetic algorithm, an increasingly popular approach to highly 
non-linear multi-dimensional optimization problems, was originally 
inspired by a biological metaphor.  This tutorial will cover both 
the biological motivations, and the actual implementation and 
characteristics of the algorithm.  Genetic Programming, an 
extension well-suited to problems where the discovery of the size 
and shape of the solution is a major part of the problem, will 
also be addressed.  Particular attention will be paid to 
biological applications, and to identifying resources and software 
that will permit attendees to begin using the methods.  Dr. Koza, 
a Consulting Professor of Computer Science at Stanford, has taught 
this subject since 1988 and is the author of a standard text in 
the field.

3:00-5:45pm "Linguistic Methods in Sequence Analysis" 
 Prof. David Searls (University of Pennsylvania) 
 & Shmuel Pietrokovski (Weizmann Institute)

Approaches to sequence analysis based on linguistic methodologies 
are increasingly in evidence.  These involve the adaptation of 
tools and techniques from computational linguistics for syntactic 
pattern recognition and gene prediction, the classification of 
genetic structures and phenomena using formal language theory, the 
identification of significant vocabularies and overlapping codes 
in sequence data, and sequence comparison reflecting taxonomic and 
functional relatedness.  Dr. Searls, who holds research faculty 
appointments in both Genetics and Computer Science at Penn, 
represents the branch of this field that considers higher-order 
syntactic approaches to sequence data, while Shmuel Pietrokovski 
has studied and published with Prof. Edward Trifinov in the area 
of word-based analyses.

                      REGISTRATION FORM
Mail, with check made out to "ISMB-93", to:

               ISMB Conference, c/o J. Shavlik
               Computer Sciences Department
               University of Wisconsin
               1210 West Dayton Street
               Madison, WI 53706  USA

        ================================================

        Name____________________________________________
	
        Affiliation_____________________________________
	
        Address_________________________________________

        ________________________________________________

        ________________________________________________

        ________________________________________________

        Phone___________________________________________

        FAX_____________________________________________

        Electronic Mail_________________________________
	
        Registration Status: ____ Regular   ____ Student

        Presenting?          ____ Talk      ____  Poster
        ================================================
        TUTORIAL REGISTRATION 

        ____"Molecular Biology for Computer Scientists"
         or
        ____"Artificial Intelligence for Biologists"
         -  -  -  -  -  -  -  -  -  -  -  -  -  -  -  - 
        ____"Neural Networks, Statistics, and 
         or     Information Theory in Sequence Analysis"
        ____"Genetic Algorithms and Genetic Programming"
         or 
        ____"Linguistic Methods in Sequence Analysis"
        ================================================
        PAYMENT       (Early Registration Before June 1)

        Registration:  Early   Late	    $___________
              Regular  $100    $125	
              Student  $75     $100	
        Tutorials:     One      Two         $___________
              Regular  $50      $65	
              Student  $25      $35	
        Total:                              $___________
        ================================================
        Registration fees include conference proceedings, 
        refreshments, and general program expenses. 


                      ORGANIZING COMMITTEE
        Lawrence Hunter                              NLM 
        David Searls                  U. of Pennsylvania
        Jude Shavlik                     U. of Wisconsin

                        PROGRAM COMMITTEE
        Douglas Brutlag                      Stanford U.
        Bruce Buchanan                  U. of Pittsburgh
        Christian Burks          Los Alamos National Lab
        Fred Cohen                    U.C.-San Francisco
        Chris Fields           Inst. for Genome Research
        Michael Gribskov                  U.C.-San Diego
        Peter Karp                     SRI International
        Toni Kazic                         Washington U.
        Alan Lapedes             Los Alamos National Lab
        Richard Lathrop                MIT & Arris Corp.
        Charles Lawrence                          Baylor 
        Michael Mavrovouniotis            U. of Maryland
        George Michaels                              NIH
        Harold Morowitz                  George Mason U.
        Katsumi Nitta                               ICOT
        Mick Noordewier                       Rutgers U.
        Ross Overbeek               Argonne National Lab
        Chris Rawlings                              ICRF
        Derek Sleeman                     U. of Aberdeen
        David States                       Washington U.
        Gary Stormo                       U. of Colorado
        Ed Uberbacher             Oak Ridge National Lab
        David Waltz              Thinking Machines Corp.