research-article

ProRank: a method for detecting protein complexes

Authors:

Jose Berengueres,

Dmitry EfimovAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 209 - 216

https://doi.org/10.1145/2330163.2330193

Published: 07 July 2012 Publication History

Abstract

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. Observations show that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. This paper introduces a novel method for detecting protein-complexes from PPI by using a protein ranking algorithm (ProRank) and incorporating an evolutionary relationships between proteins in the network. The method successfully predicted 57 out of 81 benchmarked protein complexes created from the Munich Information Center for Protein Sequence (MIPS). The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of our proposed method. Datasets, programs and results are available at http://faculty.uaeu.ac.ae/nzaki/ProRank.htm.

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Cited By

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https://doi.org/10.1007/s10586-024-04632-y
Guo KChen ZLin XWu LZhan ZChen YGuo W(2023)Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational AutoencoderIEEE Transactions on Big Data10.1109/TBDATA.2022.31649169:2(569-583)Online publication date: 1-Apr-2023
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Bonomo MGiancarlo RGreco DRombo S(2022)Topological ranks reveal functional knowledge encoded in biological networks: a comparative analysisBriefings in Bioinformatics10.1093/bib/bbac10123:3Online publication date: 5-Apr-2022
https://doi.org/10.1093/bib/bbac101
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Index Terms

ProRank: a method for detecting protein complexes
1. Applied computing
  1. Life and medical sciences

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Published: 07 July 2012

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GECCO '12

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SIGEVO

GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Heydariyan AGharehchopogh FDishabi M(2024)A hybrid multi-objective algorithm based on slime mould algorithm and sine cosine algorithm for overlapping community detection in social networksCluster Computing10.1007/s10586-024-04632-y27:10(13897-13917)Online publication date: 6-Jul-2024
https://doi.org/10.1007/s10586-024-04632-y
Guo KChen ZLin XWu LZhan ZChen YGuo W(2023)Community Detection Based on Multiobjective Particle Swarm Optimization and Graph Attention Variational AutoencoderIEEE Transactions on Big Data10.1109/TBDATA.2022.31649169:2(569-583)Online publication date: 1-Apr-2023
https://doi.org/10.1109/TBDATA.2022.3164916
Bonomo MGiancarlo RGreco DRombo S(2022)Topological ranks reveal functional knowledge encoded in biological networks: a comparative analysisBriefings in Bioinformatics10.1093/bib/bbac10123:3Online publication date: 5-Apr-2022
https://doi.org/10.1093/bib/bbac101
Shang RZhao KZhang WFeng JLi YJiao L(2022)Evolutionary multiobjective overlapping community detection based on similarity matrix and node correctionApplied Soft Computing10.1016/j.asoc.2022.109397127(109397)Online publication date: Sep-2022
https://doi.org/10.1016/j.asoc.2022.109397
Chen JWang HZhao SWang YZhang Y(2021)A non-binary hierarchical tree overlapping community detection based on multi-dimensional similarityIntelligent Data Analysis10.3233/IDA-20541825:5(1099-1113)Online publication date: 15-Sep-2021
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Jokar EMosleh MKheyrandish M(2021)Overlapping community detection in complex networks using fuzzy theory, balanced link density, and label propagationExpert Systems10.1111/exsy.1292139:5Online publication date: 9-Dec-2021
https://doi.org/10.1111/exsy.12921
Zaki NSingh HMohamed E(2021)Identifying Protein Complexes in Protein-Protein Interaction Data Using Graph Convolutional NetworkIEEE Access10.1109/ACCESS.2021.31108459(123717-123726)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3110845
Ramesh ASrivatsun G(2021)Evolutionary Algorithm for overlapping community detection using a merged maximal cliques representation schemeApplied Soft Computing10.1016/j.asoc.2021.107746112(107746)Online publication date: Nov-2021
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Wang WMeng XXiang JLi M(2021)Overlapping Protein Complexes Detection Based on Multi-level Topological SimilaritiesBioinformatics Research and Applications10.1007/978-3-030-91415-8_19(215-226)Online publication date: 18-Nov-2021
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Tian YYang SZhang X(2020)An Evolutionary Multiobjective Optimization Based Fuzzy Method for Overlapping Community DetectionIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2019.294524128:11(2841-2855)Online publication date: Nov-2020
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