research-article

Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks

Authors:

Simona RomboAuthors Info & Claims

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

Pages 193 - 200

https://doi.org/10.1145/2330163.2330191

Published: 07 July 2012 Publication History

Abstract

The detection of groups of proteins sharing common biological features is an important research issue, intensively investigated in the last few years, because of the insights it can give in understanding cell behavior. In this paper we present an extensive experimental evaluation campaign aiming at exploring the capability of Genetic Algorithms (GAs) to find clusters in protein-protein interaction networks, when different topological-based fitness functions are employed. A complete experimentation on the yeast protein-protein interaction network, along with a comparative evaluation of the effectiveness in detecting true complexes on the yeast and human networks, reveals GAs as a feasible and competitive computational technique to cope with this problem.

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Cited By

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https://doi.org/10.1016/B978-0-323-95502-7.00181-0
Manipur IGiordano MPiccirillo MParashuraman SMaddalena L(2021)Community Detection in Protein-Protein Interaction Networks and ApplicationsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2021.3138142(1-1)Online publication date: 2021
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Index Terms

Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks

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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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Cited By

Lazzaro IMilano M(2024)Algorithms for Graph and Network Analysis: Clustering and Search of Motifs in GraphsReference Module in Life Sciences10.1016/B978-0-323-95502-7.00181-0Online publication date: 2024
https://doi.org/10.1016/B978-0-323-95502-7.00181-0
Manipur IGiordano MPiccirillo MParashuraman SMaddalena L(2021)Community Detection in Protein-Protein Interaction Networks and ApplicationsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2021.3138142(1-1)Online publication date: 2021
https://doi.org/10.1109/TCBB.2021.3138142
Hashemi MMansoori E(2021)Identifying novel disease genes based on protein complexes and biological features2021 11th International Conference on Computer Engineering and Knowledge (ICCKE)10.1109/ICCKE54056.2021.9721466(471-475)Online publication date: 28-Oct-2021
https://doi.org/10.1109/ICCKE54056.2021.9721466
Liu GLiu QMa LShao Z(2021)Evolutionary Algorithms for Applications of Biological Networks: A ReviewIntelligent Computing Theories and Application10.1007/978-3-030-84522-3_8(102-114)Online publication date: 9-Aug-2021
https://doi.org/10.1007/978-3-030-84522-3_8
Abduljabbar DHashim SSallehuddin R(2020)Nature-inspired optimization algorithms for community detection in complex networks: a review and future trendsTelecommunication Systems10.1007/s11235-019-00636-xOnline publication date: 30-Jan-2020
https://doi.org/10.1007/s11235-019-00636-x
Ji JXiao HYang C(2020)HFADE-FMD: a hybrid approach of fireworks algorithm and differential evolution strategies for functional module detection in protein-protein interaction networksApplied Intelligence10.1007/s10489-020-01791-4Online publication date: 16-Sep-2020
https://doi.org/10.1007/s10489-020-01791-4
Abduljabbar DHashim SSallehuddin R(2019)An Enhanced Evolutionary Algorithm for Detecting Complexes in Protein Interaction Networks with Heuristic Biological OperatorRecent Advances on Soft Computing and Data Mining10.1007/978-3-030-36056-6_32(334-345)Online publication date: 5-Dec-2019
https://doi.org/10.1007/978-3-030-36056-6_32
Attea BAbdullah Q(2018)Improving the performance of evolutionary-based complex detection models in protein---protein interaction networksSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-017-2593-822:11(3721-3744)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s00500-017-2593-8
Lei XLiang J(2017)Neighbor Affinity-Based Core-Attachment Method to Detect Protein Complexes in Dynamic PPI NetworksMolecules10.3390/molecules2207122322:7(1223)Online publication date: 24-Jul-2017
https://doi.org/10.3390/molecules22071223
Fassetti FRombo SSerrao CFassetti FRombo SSerrao C(2017)Problems and TechniquesDiscriminative Pattern Discovery on Biological Networks10.1007/978-3-319-63477-7_2(9-20)Online publication date: 2-Sep-2017
https://doi.org/10.1007/978-3-319-63477-7_2
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