FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis
Joint Authors
Yuan, Changan
Huang, De-Shuang
Yuan, Lin
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
The epistasis is prevalent in the SNP interactions.
Some of the existing methods are focused on constructing models for two SNPs.
Other methods only find the SNPs in consideration of one-objective function.
In this paper, we present a unified fast framework integrating adaptive ant colony optimization algorithm with multiobjective functions for detecting SNP epistasis in GWAS datasets.
We compared our method with other existing methods using synthetic datasets and applied the proposed method to Late-Onset Alzheimer’s Disease dataset.
Our experimental results show that the proposed method outperforms other methods in epistasis detection, and the result of real dataset contributes to the research of mechanism underlying the disease.
American Psychological Association (APA)
Yuan, Lin& Yuan, Changan& Huang, De-Shuang. 2017. FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis. Complexity،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142979
Modern Language Association (MLA)
Yuan, Lin…[et al.]. FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis. Complexity No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1142979
American Medical Association (AMA)
Yuan, Lin& Yuan, Changan& Huang, De-Shuang. FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis. Complexity. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1142979
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142979