Name
Title | ||
|---|---|---|
Ditgossi: A Two-Stage Invasive Tumor Growth Optimization Algorithm For The Detection Of Snp-Snp Interactions |
Abstract | ||
|---|---|---|
Detecting SNP-SNP interactions for complex diseases is a computationally complex task in genome-wide association studies (GWAS). The number of single-nucleotide polymorphism (SNP) is so large that many powerful methods can't be adopted to detect potential SNP-SNP interactions, therefore, trade-off between detection time and detection power is the key point of SNP-SNP interactions detection. In this paper, based on swarm optimization algorithm Invasive Tumor Growth Optimization (ITGO), a two-stage algorithm called DITGOssi is proposed to detect SNP-SNP interactions in case-control study, which consists of a basic DITGOssi algorithm and an improved two-stage strategy. The basic DITGOssi algorithm is a discrete ITGO algorithm, and the improved two-stage strategy has been applied to enhance the global search capability of basic DITGOssi algorithm. The experimental results in the simulation datasets indicate that our algorithm outperforms some recent algorithms in terms of detection power and computational complexity. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621096 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
swarm intelligence algorithm, GWAS, SNP-SNP interactions | Swarm behaviour,Computer science,Artificial intelligence,Optimization algorithm,SNP,Machine learning,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaiwen Tan | 1 | 0 | 0.34 |
| Shoubing Dong | 2 | 0 | 0.34 |
| jing zhou | 3 | 112 | 20.35 |
| Jinlong Hu | 4 | 5 | 4.18 |