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Abstract:
Motivation: Large amounts of protein and domain interaction data are being
produced by experimental high-throughput techniques and computational
approaches. To gain insight into the value of the provided data, we used our
new similarity measure based on the Gene Ontology (GO) to evaluate the
molecular functions and biological processes of interacting proteins or
domains. The applied measure particularly addresses the frequent annotation of
proteins or domains with multiple GO terms.
Results: Using our similarity measure, we compare predicted domain–domain and
human protein–protein interactions with experimentally derived interactions.
The results show that our similarity measure is of significant benefit in
quality assessment and confidence ranking of domain and protein networks. We
also derive useful confidence score thresholds for dividing domain interaction
predictions into subsets of low and high confidence.
