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|Title:||PLW : Probabilistic Local Walks for detecting protein complexes from protein interaction networks||Authors:||Wong, Daniel Lin-Kit
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2013||Source:||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.||Series/Report no.:||BMC genomics||Abstract:||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.
|ISSN:||1471-2164||DOI:||10.1186/1471-2164-14-S5-S15||Schools:||School of Computer Engineering||Rights:||© 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.
|Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||SCSE Journal Articles|
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