From: corruble@lpia9.ibp.fr
Subject: CFP: IJCAI 93, WORKSHOP "ARTIFICIAL INTELLIGENCE and the GENOME"
Reply-To: irina1@laforia.ibp.fr
Organization: Universite Paris VI/Paris VII
Date: Thu, 17 Dec 1992 15:13:57 GMT




*****************        CALL FOR PAPERS        ************************


	    WORKSHOP "ARTIFICIAL INTELLIGENCE and the GENOME"

	at the International Joint Conference on Artificial Intelligence

				IJCAI-93 

			August 29 - September 3, 1993

		            Chambery, FRANCE

There is a great deal of intellectual excitement in molecular biology (MB)
right now. There has been an explosion of new knowledge due to the advent of
the Human Genome Program. Traditional methods of computational molecular
biology can hardly cope with important complexity issues without adapting a
heuristic approach. They enable one to explicitate molecular biology knowledge
to solve a problem as well as to present the obtained solution in
biologically-meaningful terms. The computational size of many important 
biological problems overwhelms even the fastest hardware by many orders of 
magnitude. The approximate and heuristic methods of Artificial Intelligence 
have already made significant progress in these difficult problems. Perhaps 
one reason is great deal of biological knowledge is symbolic and complex in 
their organization. Another reason is the good match between biology and 
machine learning. Increasing amout of biological data and a significant lack 
of theoretical understanding suggest the use of generalization techniques to
discover "similarities" in data and to develop some pieces of theory.
On the other hand, molecular biology is a challenging real-world domain for 
artificial intelligence research, being neither trivial nor equivalent to 
solving the general problem of intelligence. This workshop is dedicated to 
support the young AI/MB field of research.


TOPICS OF INTEREST INCLUDE (BUT ARE NOT RESTRICTED TO):
-------------------------------------------------------

*** Knowledge-based approaches to molecular biology problem solving;

Molecular biology knowledge-representation issues, knowledge-based heuristics
to guide molecular biology data processing, explanation of MB data
processing results in terms of relevant MB knowledge;

*** Data/Knowledge bases for molecular biology;

Acquisition of molecular biology knowledge, building public genomic knowledge 
bases, a concept of "different view points" in the MB data processing context;

*** Generalization techniques applied to molecular biology problem solving; 

Machine learning techniques as well as neural network techniques, supervised
learning versus non-supervised learning, scaling properties of different
generalization techniques applied to MB problems;

*** Biological sequence analysis;

AI-based methods for sequence alignment, motif finding, etc., knowledge-guided
alignment, comparison of AI-based methods for sequence analysis with the
methods
of computational biology;

*** Prediction of DNA protein coding regions and regulatory sites using
AI-methods;

Machine learning techniques, neural networks, grammar-based approaches, etc.;

*** Predicting protein folding using AI-methods;

Predicting secondary, super-secondary, tertiary protein structure,
construction protein folding prediction theories by examples;

*** Predicting gene/protein functions using AI-methods;

Complexity of the function prediction problem, understanding the
structure/function relationship in biologically-meaningful examples,
structure/functions patterns, attempts toward description of functional space;

*** Similarity and homology;

Similarity measures for gene/protein class construction, knowledge-based
similarity measures, similarity versus homology, inferring evolutionary trees;

*** Other perspective approaches to classify and predict properties of
MB sequences;

Information-theoretic approach, standard non-parametric statistical
analysis, Hidden Markov models and statistical physics methods;


INVITED TALKS:
--------------

L. Hunter, NLM, AI problems in finding genetic sequence motifs

J. Shavlik, U. of Wisconsin, Learning important relations in 
protein structures

B. Buchanan, U. of Pittsburgh, to be determined

R. Lathrop, MIT, to be determined

Y. Kodratoff, U. Paris-Sud, to be determined

J.-G. Ganascia, U. Paris-VI, Application of machine learning 
techniques to the biological investigation viewed as a constructive 
process


SCHEDULE
----------

Papers received:		March 1, 1993
Acceptance notification:	April 1, 1993
Final papers:			June  1, 1993

WORKSHOP FORMAT:
------------------
The format of the workshop will be paper sessions with discussion 
at the end of each session, and a concluding panel. 

Prospective particitants should submit papers of five to ten pages in length.
Four paper copies are required. Those who would like to attend without a
presentation should send a one to two-page description of their relevant
research interests.

Attendance at the workshop will be limited to 30 or 40 people.
Each workshop attendee MUST HAVE REGISTERED FOR THE MAIN CONFERENCE.
An additional (low) 300 FF fee for the workshop attendance (about $60)
will be required.  One student attending the workshop normally 
(has registered for the main conference) and being in charge of taking
notes during
the entirre workshop, could be exempted from the additional 300 FF fee.
Volunteers are invited.

ORGANIZING COMMITTEE
--------------------

Buchanan, B.			(Univ. of Pittsburgh - USA)
Ganascia, J.-G., chairperson	(Univ. of Paris-VI - France)
Hunter, L. 			(National Labrary of Medicine - USA)
Lathrop, R. 			(MIT - USA)
Kodratoff, Y. 			(Univ. of Paris-Sud - France)
Shavlik, J. W. 			(Univ. of Wisconsin - USA)


PLEASE, SEND SUBMISSIONS TO:
---------------------------

Ganascia, J.-G.

LAFORIA-CNRS
University Paris-VI        
4 Place Jussieu
75252 PARIS Cedex 05 
France

Phone: (33-1)-44-27-47-23
Fax: (33-1)-44-27-70-00                 
E-mail: ganascia@laforia.ibp.fr






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