Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility.
Objectives: Positively identifying disease-associated single nucleotide polymorphism (SNP) markers in genome-wide studies entails the complex association analysis of a huge number of SNPs. Such large numbers of SNP barcode (SNP/genotype combinations) continue to pose serious computational challenges...
| Publicado en: | Journal of Biomedical Informatics Vol. 63; pp. 112 - 120 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Oct2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=118967228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118967228 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Oct2016 vid: 63 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 118967228 118967228 NLM27507088 118967228 10.1016/j.jbi.2016.08.009 NLM27507088 118967228 ppf: 112 ppct: 8 formats: tig: atl: Analysis of high-order SNP barcodes in mitochondrial D-loop for chronic dialysis susceptibility. aug: au: Yang, Cheng-Hong Lin, Yu-Da Chuang, Li-Yeh Chang, Hsueh-Wei affil: Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan sug: subj: Bar Coding Algorithms DNA Polymorphism, Genetic Hemodialysis Statistics and Numerical Data Genotype Human ab: Objectives: Positively identifying disease-associated single nucleotide polymorphism (SNP) markers in genome-wide studies entails the complex association analysis of a huge number of SNPs. Such large numbers of SNP barcode (SNP/genotype combinations) continue to pose serious computational challenges, especially for high-dimensional data.Methods: We propose a novel exploiting SNP barcode method based on differential evolution, termed IDE (improved differential evolution). IDE uses a "top combination strategy" to improve the ability of differential evolution to explore high-order SNP barcodes in high-dimensional data.Results: We simulate disease data and use real chronic dialysis data to test four global optimization algorithms. In 48 simulated disease models, we show that IDE outperforms existing global optimization algorithms in terms of exploring ability and power to detect the specific SNP/genotype combinations with a maximum difference between cases and controls. In real data, we show that IDE can be used to evaluate the relative effects of each individual SNP on disease susceptibility.Conclusion: IDE generated significant SNP barcode with less computational complexity than the other algorithms, making IDE ideally suited for analysis of high-order SNP barcodes. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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