Biomedical Engineering Reference
In-Depth Information
The Semantic Web provides a technical infrastructure for the large-scale
integrated data-mining that is necessary, but advances have also been made
in the data-mining techniques themselves, particularly in the fi elds of
chemogenomics (the study of the relationships between chemical
compounds - or drugs - and genes) and systems chemical biology [9] (a
new term relating to the integrated application of cheminformatics and
bioinformatics techniques to uncover new understanding of the systematic
effects of chemical compound on the body). Recent research in
chemogenomics has included the development of generalizable algorithms
for predicting new compound-target interactions [10], creation of
predictive networks in domain areas such as Kinases [11], and combination
approaches that can be used to predict off-target interactions and new
therapeutic uses for drugs [12]. However, application of these methods
beyond the original research has been limited by lack of integrated access
to public data. Little research has been done in Systems Chemical Biology
outside of chemogenomics, for instance relating compounds to side effects,
pathways, or disease states, primarily due to the lack of available tools and
resources for relating compounds to entities other than targets or genes [9].
Chem2Bio2RDF was designed to address these gaps in data accessibility
by providing access to a wide range of data sets covering compounds,
drugs, targets, genes, assays, diseases, side effects, and pathways in a
single, integrated format.
18.3 Implementation challenges
The main issues of implementing Chem2Bio2RDF were those that plague
any implementation that uses emerging technologies: which particular
technologies to employ and which to reject (at least initially). For
Chem2Bio2RDF, this boiled down to four questions: (1) how should the
data be stored and accessed; (2) where should the data be stored; (3) how
to organize the data, and whether an OWL ontology should be used to
semantically annotate the data; and (4) how to address data quality and
equivalence across sets.
18.3.1 Data storage and access
Prior to our Semantic Web implementation, we stored a variety of
public data sets in a PostgreSQL relational database enhanced with
cheminformatics search functionality using the gNova CHORD cartridge
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