PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks

Results: We designed a novel method called Probabilistic Local Walks (PLW) which clusters regions in a PPI network with high functional similarity to find protein complex cores with high precision and efficiency in O(|V| log |V| + |E|) time. A seed selection strategy, which prioritises seeds with...

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Main Authors:	Wong, Daniel Lin-Kit, Li, Xiaoli, Wu, Min, Zheng, Jie, Ng, See-Kiong
Other Authors:	School of Computer Engineering
Format:	Article
Language:	English
Published:	2013
Subjects:	DRNTU::Engineering::Computer science and engineering
Online Access:	https://hdl.handle.net/10356/80062 http://hdl.handle.net/10220/17886
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-800622022-02-16T16:27:40Z PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks Wong, Daniel Lin-Kit Li, Xiaoli Wu, Min Zheng, Jie Ng, See-Kiong School of Computer Engineering DRNTU::Engineering::Computer science and engineering Results: We designed a novel method called Probabilistic Local Walks (PLW) which clusters regions in a PPI network with high functional similarity to find protein complex cores with high precision and efficiency in O(\|V\| log \|V\| + \|E\|) time. A seed selection strategy, which prioritises seeds with dense neighbourhoods, was devised. We defined a topological measure, called common neighbour similarity, to estimate the functional similarity of two proteins given the number of their common neighbours. Conclusions: Our proposed PLW algorithm achieved the highest F-measure (recall and precision) when compared to 11 state-of-the-art methods on yeast protein interaction data, with an improvement of 16.7% over the next highest score. Our experiments also demonstrated that our seed selection strategy is able to increase algorithm precision when applied to three previous protein complex mining techniques. Background: Many biological processes are carried out by proteins interacting with each other in the form of protein complexes. However, large-scale detection of protein complexes has remained constrained by experimental limitations. As such, computational detection of protein complexes by applying clustering algorithms on the abundantly available protein-protein interaction (PPI) networks is an important alternative. However, many current algorithms have overlooked the importance of selecting seeds for expansion into clusters without excluding important proteins and including many noisy ones, while ensuring a high degree of functional homogeneity amongst the proteins detected for the complexes. MOE (Min. of Education, S’pore) ASTAR (Agency for Sci., Tech. and Research, S’pore) Published version 2013-11-28T05:50:38Z 2019-12-06T13:39:50Z 2013-11-28T05:50:38Z 2019-12-06T13:39:50Z 2013 2013 Journal Article Wong, D. L., Li, X., Wu, M., Zheng, J., & Ng, S. (2013). PLW: Probabilistic Local Walks for detecting protein complexes from protein interaction networks. BMC genomics, 14(Suppl 5):S15. 1471-2164 https://hdl.handle.net/10356/80062 http://hdl.handle.net/10220/17886 10.1186/1471-2164-14-S5-S15 24564427 en BMC genomics © 2013 Wong et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BioMed Central's Open Data policy. Unless otherwise stated, data included in published open access articles are distributed under the terms of the Creative Commons CC0 1.0 Public Domain Dedication waiver. This applies to data included in the article, its reference list(s) and its additional files. application/pdf application/octet-stream application/octet-stream application/octet-stream application/octet-stream application/octet-stream application/octet-stream text/plain text/plain text/plain application/octet-stream text/plain text/plain application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Computer science and engineering
spellingShingle	DRNTU::Engineering::Computer science and engineering Wong, Daniel Lin-Kit Li, Xiaoli Wu, Min Zheng, Jie Ng, See-Kiong PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks
description	Results: We designed a novel method called Probabilistic Local Walks (PLW) which clusters regions in a PPI network with high functional similarity to find protein complex cores with high precision and efficiency in O(\|V\| log \|V\| + \|E\|) time. A seed selection strategy, which prioritises seeds with dense neighbourhoods, was devised. We defined a topological measure, called common neighbour similarity, to estimate the functional similarity of two proteins given the number of their common neighbours.
author2	School of Computer Engineering
author_facet	School of Computer Engineering Wong, Daniel Lin-Kit Li, Xiaoli Wu, Min Zheng, Jie Ng, See-Kiong
format	Article
author	Wong, Daniel Lin-Kit Li, Xiaoli Wu, Min Zheng, Jie Ng, See-Kiong
author_sort	Wong, Daniel Lin-Kit
title	PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks
title_short	PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks
title_full	PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks
title_fullStr	PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks
title_full_unstemmed	PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks
title_sort	plw : probabilistic local walks for detecting protein complexes from protein interaction networks
publishDate	2013
url	https://hdl.handle.net/10356/80062 http://hdl.handle.net/10220/17886
_version_	1725985696427016192

PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks

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