title: Enhancing the functional content of eukaryotic protein interaction networks.
abstract: Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS) to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.
authors.affiliation: Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America; Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
authors.forename: Gaurav
authors.initials: G
authors.lastname: Pandey
authors.affiliation: Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America; Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, United States of America.
authors.forename: Sonali
authors.initials: S
authors.lastname: Arora
authors.affiliation: JP Morgan Securities Plc., London, United Kingdom.
authors.forename: Sahil
authors.initials: S
authors.lastname: Manocha
authors.affiliation: Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America; Gladstone Institutes, University of California San Francisco, San Francisco, CA, United States of America.
authors.forename: Sean
authors.initials: S
authors.lastname: Whalen
mesh_terms: D000465:Algorithms; D056890:Eukaryota / Q000378:metabolism*; D011485:Protein Binding; D011506:Proteins / Q000378:metabolism*
chemical_list: D011506:Proteins