Biology Reference
In-Depth Information
ready exist. These include ontologies for naming proteins, for the anat-
omy of ticks, for microarray experiment conditions, for clinical symp-
toms, for human diseases, for fl y taxonomy, for metabolic pathways, and
for systems biology (just to name a few).
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Most of these ontologies are
also OWLs. Indeed, many of the most sophisticated attempts to move
toward the Semantic Web involve biological applications. For instance,
a team at the Cincinnati Children's Hospital Medical Center employed
a Semantic Web consultant to help them discover the underlying causes
of cardiovascular diseases. Gathering data from a variety of biologi-
cal databases, the team used open source Semantic Web software (Jena
and Protégé) to integrate them into a common format. “The researchers
then prioritized the hundreds of genes that might be involved with car-
diac function by applying a ranking algorithm somewhat similar to the
one Google uses to rank Web pages of search results.”
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Pharmaceuti-
cal companies, including Eli Lily and Pfi zer, have also been involved in
Semantic Web development efforts directed at drug discovery. The data
richness of biology has made it the primary fi eld for testing new Seman-
tic Web technologies.
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Bio-ontologies, however, not only entailed a new set of language rules,
but also fostered new ways of thinking about how to represent and in-
terrelate information on the web. In the late 1990s, it became clear that
understanding the human genome would mean not only building long
lists of sequences and genes, but also understanding how they interacted
with one another: biology shifted from static sequences and structures
to dynamics. Deciphering gene networks and metabolic pathways be-
came crucial. In May 2000, Eric Neumann, Aviv Regev, Joanne Luciano,
and Vincent Schachter founded the BioPathways Consortium with the
aim of promoting research on informatic technologies to capture, or-
ganize, and use pathway-related information. In the 1990s, Neumann
had worked on biological problems using semantics, XML, and HTML
tools. When Neumann encountered the problem of representing molec-
ular pathways, he was reminded of Berners-Lee's Semantic Web ideas:
[They] especially made sense in the context of trying to put to-
gether molecular pathway models, which are really semantically-
defi ned graphs. In 2000 we took a stab at representing these
using the N3 short-hand, and loading them into his [RDF]-tool.
From this point on, I was a Semantic Web convert.
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Looking for a data-graph storage system for his company's informat-
ics needs, Neumann began a collaboration with Oracle to test its new
