MINING GENE-TO-GENE RELATIONSHIPS FROM THE BIBLIOME

Jan Komorowski

Department of Computer and Information Science
Norwegian University of Science and Technology
Trondheim, Norway
and
Polish-Japanese Institute of Information Technology
Warsaw, Poland

We have carried out automated extraction of explicit and implicit
biomedical knowledge from publicly available gene and text databases
to create a gene-to-gene co-citation network for 13,712 named human
genes by automated analysis of titles and abstracts in over 10 million
MEDLINE records.  The associations between genes have been annotated
by linking genes to terms from the medical subject headings (MeSH)
index and terms from the gene ontology (GO).  The extracted database
and accompanying web-tools for gene-expression analysis have
collectively been named "PubGene".  We validated the extracted
networks by three large-scale experiments showing that co-occurrence
reflects biologically meaningful relationships, thus providing an
approach to extract and structure known biology.  We validated the
applicability of the tools by analyzing two publicly available
microarray data sets. The publicly available tool may be accessed at:
http://www.PubGene.org/  

In the talk I shall present the principles behind the design of the
system and some approaches to its validation.  One of the immediate
conclusions is that the lack of free access to all published hampers
the developments of such tools.

This is joint work with T-K. Jenssen, A. Laegreid and E. Hovig.