FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis
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 optimizati...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/5024867 |
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| author | Lin Yuan Chang-An Yuan De-Shuang Huang |
| author_facet | Lin Yuan Chang-An Yuan De-Shuang Huang |
| author_sort | Lin Yuan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ceffaeadacbb463d9fe6075b0dc31574 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-ceffaeadacbb463d9fe6075b0dc315742025-02-03T06:05:26ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/50248675024867FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP EpistasisLin Yuan0Chang-An Yuan1De-Shuang Huang2Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai 201804, ChinaScience Computing and Intelligent Information Processing of Guang Xi Higher Education Key Laboratory, Guangxi Teachers Education University, Nanning, Guangxi 530001, ChinaInstitute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai 201804, ChinaThe 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.http://dx.doi.org/10.1155/2017/5024867 |
| spellingShingle | Lin Yuan Chang-An Yuan De-Shuang Huang FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis Complexity |
| title | FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis |
| title_full | FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis |
| title_fullStr | FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis |
| title_full_unstemmed | FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis |
| title_short | FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis |
| title_sort | faacose a fast adaptive ant colony optimization algorithm for detecting snp epistasis |
| url | http://dx.doi.org/10.1155/2017/5024867 |
| work_keys_str_mv | AT linyuan faacoseafastadaptiveantcolonyoptimizationalgorithmfordetectingsnpepistasis AT changanyuan faacoseafastadaptiveantcolonyoptimizationalgorithmfordetectingsnpepistasis AT deshuanghuang faacoseafastadaptiveantcolonyoptimizationalgorithmfordetectingsnpepistasis |