Integration of relational and hierarchical network information for protein function prediction.

BMC Bioinformatics
Xiaoyu JiangEric D Kolaczyk

Abstract

In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this f...Continue Reading

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Jul 16, 2010·Biology of Reproduction·Dipanwita Roy ChoudhuryJohn R McCarrey
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Aug 8, 2018·Bioinformatics·Jeffrey M Yunes, Patricia C Babbitt
Jan 1, 2014·ISRN Bioinformatics·Giorgio Valentini

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Software Mentioned

GNEA
GSEA
Matlab

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