ArticlePloS one2012
An improved PSO algorithm for generating protective SNP barcodes in breast cancer.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
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- A gene selection algorithm for microarray cancer classification using an improved particle swarm optimization.Scientific reports · 2024Article
- Applications of artificial intelligence-machine learning for detection of stress: a critical overview.Molecular psychiatry · 2024Review
- A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases.Computational and mathematical methods in medicine · 2022Article
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- Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm.BioMed research international · 2019Article
- Interaction of MRE11 and Clinicopathologic Characteristics in Recurrence of Breast Cancer: Individual and Cumulated Receiver Operating Characteristic Analyses.BioMed research international · 2017Article
- Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks.BioMed research international · 2016Article
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- An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction.BMC genomics · 2015Article
- High order gene-gene interactions in eight single nucleotide polymorphisms of renin-angiotensin system genes for hypertension association study.BioMed research international · 2015Article
- An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.BioMed research international · 2015Article
- The Combinational Polymorphisms of ORAI1 Gene Are Associated with Preventive Models of Breast Cancer in the Taiwanese.BioMed research international · 2015Article
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- Identifying association model for single-nucleotide polymorphisms of ORAI1 gene for breast cancer.Cancer cell international · 2014Article
- A novel strategy for gene selection of microarray data based on gene-to-class sensitivity information.PloS one · 2014Article
- Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm.Annals of general psychiatry · 2014Article
- Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions.BioMed research international · 2014Article
- Particle swarm optimization algorithm for analyzing SNP-SNP interaction of renin-angiotensin system genes against hypertension.Molecular biology reports · 2013Article
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4 authors.
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Abstract
backgroundPossible single nucleotide polymorphism (SNP) interactions in breast cancer are usually not investigated in genome-wide association studies. Previously, we proposed a particle swarm optimization (PSO) method to compute these kinds of SNP interactions. However, this PSO does not guarantee to find the best result in every implement, especially when high-dimensional data is investigated for SNP-SNP interactions. METHODOLOGY/PRINCIPAL
findingsIn this study, we propose IPSO algorithm to improve the reliability of PSO for the identification of the best protective SNP barcodes (SNP combinations and genotypes with maximum difference between cases and controls) associated with breast cancer. SNP barcodes containing different numbers of SNPs were computed. The top five SNP barcode results are retained for computing the next SNP barcode with a one-SNP-increase for each processing step. Based on the simulated data for 23 SNPs of six steroid hormone metabolisms and signalling-related genes, the performance of our proposed IPSO algorithm is evaluated. Among 23 SNPs, 13 SNPs displayed significant odds ratio (OR) values (1.268 to 0.848; p<0.05) for breast cancer. Based on IPSO algorithm, the jointed effect in terms of SNP barcodes with two to seven SNPs show significantly decreasing OR values (0.84 to 0.57; p<0.05 to 0.001). Using PSO algorithm, two to four SNPs show significantly decreasing OR values (0.84 to 0.77; p<0.05 to 0.001). Based on the results of 20 simulations, medians of the maximum differences for each SNP barcode generated by IPSO are higher than by PSO. The interquartile ranges of the boxplot, as well as the upper and lower hinges for each n-SNP barcode (n = 3∼10) are more narrow in IPSO than in PSO, suggesting that IPSO is highly reliable for SNP barcode identification. CONCLUSIONS/SIGNIFICANCE: Overall, the proposed IPSO algorithm is robust to provide exact identification of the best protective SNP barcodes for breast cancer.
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