Title: Identification and prediction of functional protein modules using a bi-level community detection algorithm

Authors: Suely Oliveira; Rahil Sharma

Addresses: Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA ' Department of Computer Science, The University of Iowa, Iowa City, IA 52242, USA

Abstract: Identifying functional modules is believed to reveal most cellular processes. There have been many computational approaches to investigate the underlying biological structures. We shall use community detection algorithm which we present in a bi-level algorithmic framework to accurately identify protein complexes in less computational time. We call this algorithm bi-level label propagation algorithm (BLLP). Using this algorithm, we extract 123 communities from a protein-protein interaction (PPI) network involving 2361 proteins and 7182 interactions in Saccharomyces cerevisiae i.e. yeast. Based on these communities found, we make predictions of functional modules for 57 uncharacterised proteins in our dataset, with 80%+ accuracy. We also perform a comparative study by applying various well-known community detection algorithms on the PPI yeast network. We conclude that, BLLP algorithm extracts more accurate community structures from PPI yeast networks in less computational time.

Keywords: community detection; label propagation; bi-level algorithms; computational proteomics; bioinformatics; identification; prediction; functional protein modules; protein-protein interaction; PPI networks; Saccharomyces cerevisiae; yeast; community structures.

DOI: 10.1504/IJBRA.2016.077124

International Journal of Bioinformatics Research and Applications, 2016 Vol.12 No.2, pp.129 - 148

Received: 05 Mar 2015
Accepted: 10 Dec 2015

Published online: 19 Jun 2016 *

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