Name
Title | ||
|---|---|---|
FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/5024867 | COMPLEXITY |
Field | DocType | Volume |
Ant colony optimization algorithms,Epistasis,Algorithm,Genome-wide association study,Single-nucleotide polymorphism,Artificial intelligence,SNP,Mathematics,Machine learning | Journal | 2017 |
ISSN | Citations | PageRank |
1076-2787 | 4 | 0.49 |
References | Authors | |
12 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan, L. | 1 | 17 | 4.32 |
| Chang-an Yuan | 2 | 85 | 9.88 |
| De-Shuang Huang | 3 | 5532 | 357.50 |