TY - GEN
T1 - ABCAE
T2 - 19th International Symposium on Bioinformatics Research and Applications, ISBRA 2023
AU - Ren, Qianqian
AU - Li, Yahan
AU - Li, Feng
AU - Liu, Jin Xing
AU - Shang, Junliang
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Adaptive exploitation
KW - Artificial bee colony
KW - Complex disease Epistatic interaction
UR - https://www.scopus.com/pages/publications/85174255096
U2 - 10.1007/978-981-99-7074-2_15
DO - 10.1007/978-981-99-7074-2_15
M3 - 会议稿件
AN - SCOPUS:85174255096
SN - 9789819970735
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 190
EP - 201
BT - Bioinformatics Research and Applications - 19th International Symposium, ISBRA 2023, Proceedings
A2 - Guo, Xuan
A2 - Mangul, Serghei
A2 - Patterson, Murray
A2 - Zelikovsky, Alexander
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 9 October 2023 through 12 October 2023
ER -