CFP for "Motifs in Biology: Analysis of Ambiguous Data"

Toni Kazic toni at athe.wustl.edu
Mon Apr 26 15:23:36 EST 1993


-----------------  revised cfp ----------------

		Motifs in Biology:  Analysis of Ambiguous Data

	  Hawaii International Conference on System Sciences - 27
		       Biotechnology Computing Track
		     Maui, Hawaii, January 4 - 7, 1994

	Co-chairs:  Lloyd Allison, Monash University, Australia
                    Toni Kazic, Washington University, USA
                    David States, Washington University, USA


The Hawaii International Conference on System Sciences (HICSS) - 27 announces
a call for papers for the Biotechnology Computing Minitrack on the topic of
Motifs in Biology. This minitrack will focus on finding, classifying, and
testing motifs in the organization, structure and dynamics of biological
systems.

The use, reuse, and recombination of motifs in biology is fundamental to
genetics and evolution.  By ``motif'' we mean any nonrandom, recurrent
organizational pattern, at any level of biological complexity.  Classification
in biology depends on the recognition of motifs, whether these are structural
or ecological features of an organism in evolutionary taxonomy, spatial or
temporal motifs in embryology and development, or similarities in catalytic
mechanisms in enzymology.  Motif induction, representation, and recognition in
biological databases is a challenging area of computational biology.  Often
the high level representation of pattern information is as important as the
ability to recognize a motif.  In other cases the problem is to assemble
scattered information of many types into a credible pattern.  Many techniques
have been used to induce patterns from anonymous data, ranging from
stochastic-search to a variety of classification schemes.  A second task is
the recognition of instances of hypothesized patterns in apparently related
data.  Both activities take place against a background of inherent biological
variation and diversity, experimental error and various sources of "noise".
Variation is present both in the biological material and the data.  It
produces multiple "versions" of data items.  Information from different
sources may be contradictory.  All this confounds the induction and
recognition of motifs.  Success is strongly determined by how variation in
biological data is treated computationally.  Outputs are of little use unless
accompanied by indications of their biological and statistical significance.

The identification of novel motifs in complex data is the essence of
scientific discovery. The purpose of this minitrack is to bring pattern
recognition, classification, variation handling and information extraction
technologies together with motif discovery applications in biological domains.
Emphasis will be given to biological areas which are still unfamiliar to
computer scientists, such as developmental biology, and which might benefit
from advanced pattern induction techniques.  Papers should describe
applications or algorithms for real biological systems, not abstractions of
biological problems which conveniently remove variation to simplify the
computation or increase the elegance of the abstraction.  Thus, papers
describing algorithms or heuristics, statistical or other measures of
biological merit which treat either inherent biological variation or gaps or
uncertainties in data are particularly welcome.  Algorithms should have been
run on real biological data, or on simulated data with explicitly stated
assumptions, or on both.  Computational, statistical and other methods should
be described in sufficient detail for another worker to reproduce them.

We believe the time is ripe to begin considering the problem of motif
recognition in more general terms.  Though the literature has been preoccupied
with the recognition of linear motifs in DNA and proteins, there is a growing
appreciation of the importance of spatial, temporal and functional motifs.
Representation of motifs and the underlying data structures can have profound
consequences for our ability to compute with them.  Appropriate representation
can reveal motifs which would be obscured in other data structures.  Alert
biologists are beginning to wonder what computer science can offer to aid
recognition of complex, multifaceted motifs.  The imperfections of biological
data mean that the straightforward application of computational techniques,
which usually assume perfect data or complete matching, is inappropriate.

Biological application include:

    o  Identification and characterization of signalling or structural
		motifs in linear sequences or chromosomes
    o  Analysis of folding patterns in DNA and RNA.
    o  Analysis of packing and folding patterns in protein tertiary structure
    o  Recognition of motifs in nonlinear data such as 
		physiological networks (topological, dynamic and regulatory)
    o  Analysis of temporal and spatial patterns in development
    o  Molecular approaches to systematic biology
    o  Origin and evolution of motifs.

Computational techniques include:

    o  Bayesian and other statistical classification methods
    o  Statistical and non-logical retrieval methods for databases
    o  Hidden Markov models
    o  Information theory and algorithmic complexity
    o  Genetic algorithms
    o  Artificial neural nets and techniques to extract information from them
    o  Novel algorithms and data structures
    o  Graph topology approaches
    o  Database dredging 
    o  Case-based reasoning
    o  Formal grammars of biological structure and function
    o  Validation techniques
    o  Construction and use of artificial data sets
    o  Data representation
    o  Parallel computation




INSTRUCTIONS FOR AUTHORS

A miniabstract consisting of a title, list of authors and two or three
sentences describing the manuscript's application area and techniques is
requested.  Manuscripts should be 22-26 typewritten, double-spaced pages in 10
or 12 point type.  The final form for the printed papers will be 10-12 pages,
10 point, single spaced, in two columns.  Authors are encouraged to check
their manuscript prior to submission to be sure it falls within these final
boundaries.  Papers must not have been previously presented or published, nor
currently submitted for journal publication.  Once accepted to the conference,
a paper may be submitted for journal publication. Each manuscript will be
refereed by five reviewers.  Manuscripts should include a title page that
identifies the title of the paper, the full name(s) of the author(s),
affiliation(s), complete mailing and electronic address(es), telephone
number(s) and a 300 word abstract of the paper.


Deadlines:

* Miniabstract is requested by mid-May, 1993.
* Six copies of the manuscript are due by June 7, 1993 
* Notification of accepted papers by August 31, 1993 
* Accepted camera ready manuscripts are due by October 1, 1993

Note: The miniabstract should be sent by electronic mail.  Fax or electronic
submission of manuscripts is not acceptable.

Send submissions and questions regarding this minitrack to any of the cochairs:

Lloyd Allison
Dept. Computer Science
Monash University 
AUSTRALIA 3168
lloyd at cs.monash.edu.au
61 3 565 5205  (tel)
61 3 565 5146  (fax)



Toni Kazic
Institute for Biomedical Computing
Box 8036
Washington University School of Medicine
700 South Euclid Ave.
St. Louis MO  63110  USA
toni at athe.wustl.edu
314-362-3121  (tel)
314-362-0234  (fax)




David States
Institute for Biomedical Computing
Box 8068
Washington University School of Medicine
700 South Euclid Ave.
St. Louis MO  63110  USA
states at ibc.wustl.edu
314-362-2138  (tel)
314-362-0234  (fax)



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