research-article

A Fast Gene Expression Analysis using Parallel Biclustering and Distributed Triclustering Approach

Authors:

Hasin A. Ahmed,

Dhruba K. Bhattacharyya,

Jugal K. KalitaAuthors Info & Claims

ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

Article No.: 122, Pages 1 - 6

https://doi.org/10.1145/2905055.2905182

Published: 04 March 2016 Publication History

Abstract

Biclustering or simultaneous clustering aims to mine rowise and columwise a G×S dataset into groups of genes coexpressed across a subset of conditions. Triclustering is a recent advancement in unsupervised learning, which groups genes under a subset of conditions and time points over G×S×T plane. With the growing size of data, the cost of tricluster extraction becomes too high, which demands for a cost-effective triclustering method by distributing the computational load is needed for obtaining optimal results. This paper presents a fast shared memory biclustering and shared nothing triclustering analysis architecture to analyze gene expression data to identify coexpressed patterns of high biological significance over GxSxT plane. The proposed triclustering approach has been found able to identify shifted, scaled, and shifted-and-scaled coexpressed patterns at minimum cost over several benchmark datasets.

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Cited By

Mandal KSarmah RBhattacharyya D(2022)POPTricExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.116336192:COnline publication date: 15-Apr-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.116336
Mondal KGhosh MFajriyah RRoy A(2022)Introducing suffix forest for mining tri-clusters from time-series dataInnovations in Systems and Software Engineering10.1007/s11334-022-00489-9Online publication date: 1-Oct-2022
https://doi.org/10.1007/s11334-022-00489-9
Lobo JHenriques RMadeira S(2021)G-Tric: generating three-way synthetic datasets with triclustering solutionsBMC Bioinformatics10.1186/s12859-020-03925-422:1Online publication date: 7-Jan-2021
https://doi.org/10.1186/s12859-020-03925-4
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ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies

March 2016

843 pages

ISBN:9781450339629

DOI:10.1145/2905055

Copyright © 2016 ACM.

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Published: 04 March 2016

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ICTCS '16

ICTCS '16: Second International Conference on Information and Communication Technology for Competitive Strategies

March 4 - 5, 2016

Udaipur, India

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Overall Acceptance Rate 97 of 270 submissions, 36%

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Cited By

Mandal KSarmah RBhattacharyya D(2022)POPTricExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.116336192:COnline publication date: 15-Apr-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.116336
Mondal KGhosh MFajriyah RRoy A(2022)Introducing suffix forest for mining tri-clusters from time-series dataInnovations in Systems and Software Engineering10.1007/s11334-022-00489-9Online publication date: 1-Oct-2022
https://doi.org/10.1007/s11334-022-00489-9
Lobo JHenriques RMadeira S(2021)G-Tric: generating three-way synthetic datasets with triclustering solutionsBMC Bioinformatics10.1186/s12859-020-03925-422:1Online publication date: 7-Jan-2021
https://doi.org/10.1186/s12859-020-03925-4
Apriana DSiswantining TSarwinda DSoemartojo S(2020)Triclustering Analysis Using Extended Dimension Iterative Signature Algorithm (EDISA) on Lung Disease Gene Expression Data2020 3rd International Conference on Biomedical Engineering (IBIOMED)10.1109/IBIOMED50285.2020.9487606(7-12)Online publication date: 6-Oct-2020
https://doi.org/10.1109/IBIOMED50285.2020.9487606
Biswal BPatra SMohapatra AVipsita S(2020)TriRNSC: triclustering of gene expression microarray data using restricted neighbourhood searchIET Systems Biology10.1049/iet-syb.2020.002414:6(323-333)Online publication date: 13-Oct-2020
https://doi.org/10.1049/iet-syb.2020.0024
Mohapatra SSarkar MMohapatra ABiswal B(2020)Triclustering of Gene Expression Microarray Data Using Coarse-Grained Parallel Genetic AlgorithmInventive Communication and Computational Technologies10.1007/978-981-15-0146-3_50(529-539)Online publication date: 30-Jan-2020
https://doi.org/10.1007/978-981-15-0146-3_50
Biswal BMohapatra AVipsita S(2019)Triclustering of gene expression microarray data using coarse grained and dynamic deme based parallel genetic approachEvolutionary Intelligence10.1007/s12065-019-00330-6Online publication date: 28-Nov-2019
https://doi.org/10.1007/s12065-019-00330-6
Henriques RMadeira S(2018)Triclustering Algorithms for Three-Dimensional Data AnalysisACM Computing Surveys10.1145/319583351:5(1-43)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3195833

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