Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks

Mengfei Cao, Hao Zhang, Jisoo Park, Noah M Daniels, Mark E Crovella, Lenore J Cowen and Benjamin Hescott

PLOS ONE, 2013, vol. 8, issue 10, 1-12

Abstract: In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0076339

DOI: 10.1371/journal.pone.0076339

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