ArticleScientific reports2017
Niche harmony search algorithm for detecting complex disease associated high-order SNP combinations.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
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13 citing papers in PubMed.
- A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search.Interdisciplinary sciences, computational life sciences · 2024Article
- Cluster effect for SNP-SNP interaction pairs for predicting complex traits.Scientific reports · 2024Article
- SEEI: spherical evolution with feedback mechanism for identifying epistatic interactions.BMC genomics · 2024Article
- Article
- Digenic Analysis Finds Highly Interactive Genetic Variants Underlying Polygenic Traits.Medical research archives · 2023Article
- Article
- Article
- Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection.Genes · 2022Article
- A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases.Computational and mathematical methods in medicine · 2022Article
- GEP-EpiSeeker: a gene expression programming-based method for epistatic interaction detection in genome-wide association studies.BMC genomics · 2021Article
- SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease.BioMed research international · 2020Article
- Self-Adjusting Ant Colony Optimization Based on Information Entropy for Detecting Epistatic Interactions.Genes · 2019Article
- FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies.Genes · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide association study is especially challenging in detecting high-order disease-causing models due to model diversity, possible low or even no marginal effect of the model, and extraordinary search and computations. In this paper, we propose a niche harmony search algorithm where joint entropy is utilized as a heuristic factor to guide the search for low or no marginal effect model, and two computationally lightweight scores are selected to evaluate and adapt to diverse of disease models. In order to obtain all possible suspected pathogenic models, niche technique merges with HS, which serves as a taboo region to avoid HS trapping into local search. From the resultant set of candidate SNP-combinations, we use G-test statistic for testing true positives. Experiments were performed on twenty typical simulation datasets in which 12 models are with marginal effect and eight ones are with no marginal effect. Our results indicate that the proposed algorithm has very high detection power for searching suspected disease models in the first stage and it is superior to some typical existing approaches in both detection power and CPU runtime for all these datasets. Application to age-related macular degeneration (AMD) demonstrates our method is promising in detecting high-order disease-causing models.
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