Refining modules to determine functionally significant clusters in molecular networks

Background: Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a n...

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Main Authors: Kaalia, Rama, Rajapakse, Jagath Chandana
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146320
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1463202021-02-09T06:09:26Z Refining modules to determine functionally significant clusters in molecular networks Kaalia, Rama Rajapakse, Jagath Chandana School of Computer Science and Engineering Engineering::Computer science and engineering Functional Modules Module Refinement Background: Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks. Results: The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks. Conclusion: The proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets. Ministry of Education (MOE) Published version Publication of this supplement was funded by Tier-2 MOE2016-T2–1-029 grant by the Ministry of Education, Singapore. 2021-02-09T06:09:26Z 2021-02-09T06:09:26Z 2019 Journal Article Kaalia, R., & Rajapakse, J. C. (2019). Refining modules to determine functionally significant clusters in molecular networks. BMC Genomics, 20, 901-. doi:10.1186/s12864-019-6294-9 1471-2164 https://hdl.handle.net/10356/146320 10.1186/s12864-019-6294-9 31874644 2-s2.0-85077109382 20 en MOE2016-T2–1-029 BMC Genomics © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Functional Modules
Module Refinement
spellingShingle Engineering::Computer science and engineering
Functional Modules
Module Refinement
Kaalia, Rama
Rajapakse, Jagath Chandana
Refining modules to determine functionally significant clusters in molecular networks
description Background: Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks. Results: The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks. Conclusion: The proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Kaalia, Rama
Rajapakse, Jagath Chandana
format Article
author Kaalia, Rama
Rajapakse, Jagath Chandana
author_sort Kaalia, Rama
title Refining modules to determine functionally significant clusters in molecular networks
title_short Refining modules to determine functionally significant clusters in molecular networks
title_full Refining modules to determine functionally significant clusters in molecular networks
title_fullStr Refining modules to determine functionally significant clusters in molecular networks
title_full_unstemmed Refining modules to determine functionally significant clusters in molecular networks
title_sort refining modules to determine functionally significant clusters in molecular networks
publishDate 2021
url https://hdl.handle.net/10356/146320
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