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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

ABCAE: Artificial Bee Colony Algorithm with Adaptive Exploitation for Epistatic Interaction Detection

verfasst von : Qianqian Ren, Yahan Li, Feng Li, Jin-Xing Liu, Junliang Shang

Erschienen in: Bioinformatics Research and Applications

Verlag: Springer Nature Singapore

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Abstract

The detection of epistatic interactions among multiple single-nucleotide polymorphisms (SNPs) in complex diseases has posed a significant challenge in genome-wide association studies (GWAS). However, most existing methods still suffer from algorithmic limitations, such as high computational requirements and low detection ability. In the paper, we propose an artificial bee colony algorithm with adaptive exploitation (ABCAE) to address these issues in epistatic interaction detection for GWAS. An adaptive exploitation mechanism is designed and used in the onlooker stage of ABCAE. By using the adaptive exploitation mechanism, ABCAE can locally optimize the promising SNP combination area, thus effectively coping with the challenges brought by high-dimensional complex GWAS data. To demonstrate the detection ability of ABCAE, we compare it against four existing algorithms on eight epistatic models. The experimental results demonstrate that ABCAE outperforms the four existing methods in terms of detection ability.

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Vorheriges Kapitel scGASI: A Graph Autoencoder-Based Single-Cell Integration Clustering Method
Nächstes Kapitel USTAR: Improved Compression of k-mer Sets with Counters Using de Bruijn Graphs
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Metadaten
Titel
ABCAE: Artificial Bee Colony Algorithm with Adaptive Exploitation for Epistatic Interaction Detection
verfasst von
Qianqian Ren
Yahan Li
Feng Li
Jin-Xing Liu
Junliang Shang
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Bioinformatics Research and Applications

Print ISBN: 978-981-9970-73-5

Electronic ISBN: 978-981-9970-74-2

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-99-7074-2_15

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