ArticleGenes2019
Self-Adjusting Ant Colony Optimization Based on Information Entropy for Detecting Epistatic Interactions.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- ACOCMPMI: An Ant Colony Optimization Algorithm Based on Composite Multiscale Part Mutual Information for Detecting Epistatic Interactions.Human mutation · 2025Article
- Network medicine-based epistasis detection in complex diseases: ready for quantum computing.Nucleic acids research · 2024Article
- Article
- Network medicine-based epistasis detection in complex diseases: ready for quantum computing.medRxiv : the preprint server for health sciences · 2023Article
- A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases.Computational and mathematical methods in medicine · 2022Article
- Path Planning of Pattern Transfer Based on Dual-Operator and a Dual-Population Ant Colony Algorithm for Digital Mask Projection Lithography.Entropy (Basel, Switzerland) · 2020Article
- SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease.BioMed research international · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The epistatic interactions of single nucleotide polymorphisms (SNPs) are considered to be an important factor in determining the susceptibility of individuals to complex diseases. Although many methods have been proposed to detect such interactions, the development of detection algorithm is still ongoing due to the computational burden in large-scale association studies. In this paper, to deal with the intensive computing problem of detecting epistatic interactions in large-scale datasets, a self-adjusting ant colony optimization based on information entropy (IEACO) is proposed. The algorithm can automatically self-adjust the path selection strategy according to the real-time information entropy. The performance of IEACO is compared with that of ant colony optimization (ACO), AntEpiSeeker, AntMiner, and epiACO on a set of simulated datasets and a real genome-wide dataset. The results of extensive experiments show that the proposed method is superior to the other methods.
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Registered trials
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