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Authors: Aicha Boutorh and Ahmed Guessoum

Affiliation: University of Science and Technology Houari Boumedienne, Algeria

ISBN: 978-989-758-012-3

Keyword(s): Association Rule Mining, Gene-Gene Interaction, Epistasis, Grammatical Evolution, SNP.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Data Mining and Machine Learning ; Soft Computing

Abstract: An important goal of human genetics is to identify DNA sequence variations that increase or decrease specific disease susceptibility. Complex interactions among genes and environmental factors are known to play a role in common human disease etiology. Methods for association rule mining (ARM) are highly successful; especially that they produce rules which are easily interpretable. This has made them widely used in various domains. During the different stages of the knowledge discovery process, several problems are faced. It turns out that, the search characteristics of Evolutionary Algorithms make them suited to solve this kind of problems. In this study, we introduce GEARM, a novel approach for discovering association rules using Grammatical Evolution. We present the approach and evaluate it on simulated data that represents epistasis models. We show that this method improves the performance of gene-gene interaction detection.

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Paper citation in several formats:
Boutorh, A. and Guessoum, A. (2014). Grammatical Evolution Association Rule Mining to Detect Gene-Gene Interaction.In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014) ISBN 978-989-758-012-3, pages 253-258. DOI: 10.5220/0004913702530258

@conference{bioinformatics14,
author={Aicha Boutorh. and Ahmed Guessoum.},
title={Grammatical Evolution Association Rule Mining to Detect Gene-Gene Interaction},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)},
year={2014},
pages={253-258},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004913702530258},
isbn={978-989-758-012-3},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2014)
TI - Grammatical Evolution Association Rule Mining to Detect Gene-Gene Interaction
SN - 978-989-758-012-3
AU - Boutorh, A.
AU - Guessoum, A.
PY - 2014
SP - 253
EP - 258
DO - 10.5220/0004913702530258

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