ArticleInterdisciplinary sciences, computational life sciences2022
A Novel Multitasking Ant Colony Optimization Method for Detecting Multiorder SNP Interactions.
Article in Interdisciplinary sciences, computational life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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
- SimHOEPI: A resampling simulator for generating single nucleotide polymorphism data with a high-order epistasis model.Quantitative biology (Beijing, China) · 2024Article
- Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis.Frontiers in pharmacology · 2024Article
- Epi-SSA: A novel epistasis detection method based on a multi-objective sparrow search algorithm.PloS one · 2024Article
- A multistart tabu search-based method for feature selection in medical applications.Scientific reports · 2023Article
- Using Bayesian networks with Tabu-search algorithm to explore risk factors for hyperhomocysteinemia.Scientific reports · 2023Article
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8 authors.
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Abstract
motivationLinear or nonlinear interactions of multiple single-nucleotide polymorphisms (SNPs) play an important role in understanding the genetic basis of complex human diseases. However, combinatorial analytics in high-dimensional space makes it extremely challenging to detect multiorder SNP interactions. Most classic approaches can only perform one task (for detecting k-order SNP interactions) in each run. Since prior knowledge of a complex disease is usually not available, it is difficult to determine the value of k for detecting k-order SNP interactions.
methodsA novel multitasking ant colony optimization algorithm (named MTACO-DMSI) is proposed to detect multiorder SNP interactions, and it is divided into two stages: searching and testing. In the searching stage, multiple multiorder SNP interaction detection tasks (from 2nd-order to kth-order) are executed in parallel, and two subpopulations that separately adopt the Bayesian network-based K2-score and Jensen-Shannon divergence (JS-score) as evaluation criteria are generated for each task to improve the global search capability and the discrimination ability for various disease models. In the testing stage, the G test statistical test is adopted to further verify the authenticity of candidate solutions to reduce the error rate.
resultThree multiorder simulated disease models with different interaction effects and three real age-related macular degeneration (AMD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) datasets were used to investigate the performance of the proposed MTACO-DMSI. The experimental results show that the MTACO-DMSI has a faster search speed and higher discriminatory power for diverse simulation disease models than traditional single-task algorithms. The results on real AMD data and RA and T1D datasets indicate that MTACO-DMSI has the ability to detect multiorder SNP interactions at a genome-wide scale. Availability and implementation: https://github.com/shouhengtuo/MTACO-DMSI/.
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