Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

Chuang, Li‐Yeh; Lane, Hsien‐Yuan; Lin, Yu‐Da; Lin, Ming-Teng; Yang, Cheng‐Hong; Chang, Hsueh‐Wei

doi:10.1186/1744-859x-13-15

Cited by 19 publications

(3 citation statements)

References 54 publications

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“…Results showed QPSO-K2 algorithm performed better than stand-alone K2 and QPSO for all datasets. Chuang et al [343] compared patients with high and low facial emotion perception (FEP) performances ( = 89 and 93, resp.). A PSO algorithm was used to identify the best single-nucleotide polymorphisms (SNP) barcodes, that is, the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups.…”

Section: Medical Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

683

380

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Medical Engineeringmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

683

380

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“…Several MDR-based extension methods have been proposed [15], including MDR-ER [16], particle swarm optimization-based MDR (PBMDR) [17], class-based MDR (CMDR) [18], MOMDR [19], IMDR [20], and the empirical fuzzy MDR (EFMDR) [21]. Recently, MDR has had demonstrably superior implementation in the SNP-SNPinteraction detection of cardiovascular diseases [22], breast cancer [23], and facial emotion perceptions [24].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

2020

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The identification of interactions between single-nucleotide polymorphisms (SNP-SNP interactions) is crucial for determining human genetic disease susceptibility. With rapid technological advancements, multiobjective multifactor dimensionality reduction (MOMDR) measurements have achieved high detection success rates. However, the classification of high-or low-risk groups is central to MOMDR and has yet to be extensively studied. To address limitations in binary classification, we propose an improved fuzzy sigmoid (FS) approach that uses membership degrees in MOMDR, thus denoting it as FSMOMDR. For determining the interval of membership, our improved FS approach assesses the distance between the i th multifactor class and outcome (cases and controls). Thus, the improved FS approach enables MOMDR algorithms to determine the membership degrees of high-and low-risk groups in each multifactor class because the two-element set is extended to a specified membership interval. Moreover, the improved FS approach can handle uncertain information, which thus enables the effective detection of the m-locus combinations with similar distributions. FSMOMDR measurements can also distinguish similar frequencies among genotype combinations, thus enabling the detection of more significant SNP-SNP interactions. On the basis of the classification accuracy rate of MOMDR and results obtained from the analysis of several test data sets, we determined FSMOMDR to be superior to other MDR-based methods with respect to detection success rate. The results indicate that binary and fuzzy classifications involving MOMDR can provide insight into uncertainty in risk classification. Thus, FSMOMDR could successfully detect SNP-SNP interactions in coronary artery disease in a large data set obtained from the Wellcome Trust Case Control Consortium. We could successfully reduce uncertain information in MDR and thus suggest that membership based on the improved sigmoid function can be used to identify SNP-SNP interactions as well as obtain content knowledge.

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“…Thus, evolutionary computations have been applied to improve statistical methods for identifying significant genetic associations. Particle swarm optimization (PSO) has been applied to identify significant genetic associations for facial emotion perception [11] and hypertension [12]. Genetic algorithm (GA) has been successfully used to identify significant genetic associations for chronic dialysis [13] and breast cancer [14].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes

Yang

Moi

Lin

et al. 2016

Journal of Artificial Intelligence and Soft Computing Research

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Detecting genetic association models between single nucleotide polymorphisms (SNPs) in various disease-related genes can help to understand susceptibility to disease. Statistical tools have been widely used to detect significant genetic association models, according to their related statistical values, including odds ratio (OR), chi-square test (χ 2 ), p-value, etc. However, the high number of computations entailed in such operations may limit the capacity of such statistical tools to detect high-order genetic associations. In this study, we propose lsGA algorithm, a genetic algorithm based on local search method, to detect significant genetic association models amongst large numbers of SNP combinations. We used two disease models to simulate the large data sets considering the minor allele frequency (MAF), number of SNPs, and number of samples. The three-order epistasis models were evaluated by chi-square test (χ 2 ) to evaluate the significance (P-value < 0.05). Analysis results showed that lsGA provided higher chi-square test values than that of GA. Simple linear regression indicated that lsGA provides a significant advantage over GA, providing the highest β values and significant p-value.

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Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

Cited by 19 publications

References 54 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

Genetic Algorithm Combined with a Local Search Method for Identifying Susceptibility Genes

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