A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

Tuo, Shouheng; Chen, Hao; Liu, Haiyan

doi:10.1109/access.2019.2951700

Cited by 11 publications

(3 citation statements)

References 74 publications

(77 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the traditional epistasis detection methods have achieved good results, there are also problems such as large amount of calculation and easy over-fitting. Swarm intelligence algorithm is a global optimization method that can efficiently explore the search space [5]. Thus, swarm intelligence algorithms are widely used to detect epistatic interactions.…”

Section: Introductionmentioning

confidence: 99%

Crow Search Algorithm Based on Information Interaction for Epistasis Detection

Zhang

Shang

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: In the genome-wide association study, the interactions of single nucleotide polymorphisms (SNPs) play an important role in revealing the genetic mechanism of complex diseases, and such interaction is called epistasis or epistatic interactions. In recent years, swarm intelligence methods have been widely used to detect epistatic interactions because they can effectively deal with global optimization problems. Results: In this study, we propose a crow search algorithm based on information interaction (FICSA) to detect epistatic interactions. FICSA combines particle swarm optimization (PSO) and crow search algorithm (CSA) to balance the exploration and exploitation in the search process, which can effectively improve the ability of the algorithm to detect epistatic interactions. In addition, opposition-based learning strategy and adaptive parameters are used to further improve the performance of the algorithm. We compare FICSA with other five epistasis detection algorithms on simulated datasets and an age-related macular degeneration (AMD) dataset. The results on simulated datasets show that FICSA has better detection power, while the results on the real dataset demonstrate the effectiveness of the proposed algorithm. Conclusions: The results show that FICSA is better than other methods and can effectively detect epistatic interactions. In addition，FICSA was tested on AMD data, many of the epistatic interactions found have been proved to be related to AMD in the relevant literature. Therefore, FICSA has good performance in epistasis detection.

show abstract

Section: Introductionmentioning

confidence: 99%

Crow Search Algorithm Based on Information Interaction for Epistasis Detection

Zhang

Shang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Some are focused entirely on their methodology, comparing the different approaches, their advantages and limitations [9]- [15]. Other studies go further by also including an empirical comparison from simulation studies, although the number of methods included in these studies is more limited [16]- [20]. There are also previous publications regarding the selection of epistatic detection methods and how to integrate them in the different stages of a genetic study [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Existing Methods for High-Order Epistasis Detection

Ponte-Fernández

González-Domínguez

Carvajal-Rodríguez

et al. 2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to nonexhaustive methods when the analysis time is prohibitive.

show abstract

“…In the recent years, swarm intelligent optimization algorithms arising from natural phenomena and biological system have held high attention in the detection of diseaseassociated SNP-SNP interactions [19][20][21]. For instance, FHSA-SED [22] combines the harmony search algorithm with two scoring functions for the detection of SNP-SNP interactions.…”

Section: Introductionmentioning

confidence: 99%

SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease

Yin

Guan

Zhao

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

Detecting SNP-SNP interactions associated with disease is significant in genome-wide association study (GWAS). Owing to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power and long running time. To tackle these drawbacks, a fast self-adaptive memetic algorithm (SAMA) is proposed in this paper. In this method, the crossover, mutation, and selection of standard memetic algorithm are improved to make SAMA adapt to the detection of SNP-SNP interactions associated with disease. Furthermore, a self-adaptive local search algorithm is introduced to enhance the detecting power of the proposed method. SAMA is evaluated on a variety of simulated datasets and a real-world biological dataset, and a comparative study between it and the other four methods (FHSA-SED, AntEpiSeeker, IEACO, and DESeeker) that have been developed recently based on evolutionary algorithms is performed. The results of extensive experiments show that SAMA outperforms the other four compared methods in terms of detection power and running time.

show abstract

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

Cited by 11 publications

References 74 publications

Crow Search Algorithm Based on Information Interaction for Epistasis Detection

Crow Search Algorithm Based on Information Interaction for Epistasis Detection

Evaluation of Existing Methods for High-Order Epistasis Detection

SAMA: A Fast Self-Adaptive Memetic Algorithm for Detecting SNP-SNP Interactions Associated with Disease

Contact Info

Product

Resources

About