research-article

An insight into biological datamining based on rarity and correlation as constraints

Authors:

Souad Bouasker,

Sadok Ben Yahia,

Gayo DialloAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 3 - 10

https://doi.org/10.1145/3297280.3297281

Published: 08 April 2019 Publication History

Abstract

Association-rules mining techniques have been widely applied to identify differently expressed gene expressions among micro-array data. Rare correlated patterns are identified as efficient in generating accurate association rules. To our knowledge, no any algorithm which is able to perform this challenging task is currently available. Therefore, we designed CPMiner, a new generic method for mining interesting correlations from data. It performs the extraction of the sets of both frequent correlated and rare correlated patterns as well as their associated condensed representations according to two distinct measures: all-confidence and bond. CPMiner has been applied on processing biological data. To this end we developed CoRaM, the first unified framework dedicated to the extraction of a generic basis of Correlated-Rare association rules from gene expression data. It relies on CPMiner extracting the rare correlated patterns and both a specific discretization method and the derivation of the generic basis of the rare correlated association rules. Our proposed approach has been successfully applied on a breast-cancer Gene Expression Matrix (GSE1379) with very promising results.

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Cited By

Houari ABen Yahia S(2023)A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray dataInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01949-915:3(941-962)Online publication date: 12-Sep-2023
https://doi.org/10.1007/s13042-023-01949-9
Bouasker SInoubli WYahia SDiallo G(2020)Pregnancy Associated Breast Cancer Gene Expressions : New Insights on Their Regulation Based on Rare Correlated PatternsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2020.301523618:3(1035-1048)Online publication date: 10-Aug-2020
https://dl.acm.org/doi/10.1109/TCBB.2020.3015236

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An insight into biological datamining based on rarity and correlation as constraints

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cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

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Published: 08 April 2019

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SAC '19: The 34th ACM/SIGAPP Symposium on Applied Computing

April 8 - 12, 2019

Limassol, Cyprus

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Cited By

Houari ABen Yahia S(2023)A Top-K formal concepts-based algorithm for mining positive and negative correlation biclusters of DNA microarray dataInternational Journal of Machine Learning and Cybernetics10.1007/s13042-023-01949-915:3(941-962)Online publication date: 12-Sep-2023
https://doi.org/10.1007/s13042-023-01949-9
Bouasker SInoubli WYahia SDiallo G(2020)Pregnancy Associated Breast Cancer Gene Expressions : New Insights on Their Regulation Based on Rare Correlated PatternsIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2020.301523618:3(1035-1048)Online publication date: 10-Aug-2020
https://dl.acm.org/doi/10.1109/TCBB.2020.3015236

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