Identifying interacting genetic variations by fish-swarm logic regression.
Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivari...
| Publicado en: | BioMed Research International Vol. 2013; pp. 574735 - 574736 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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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=104090785&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104090785 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104090785 104090785 2012240561 NLM23984382 PMC3747618 104090785 ppf: 574735 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Identifying interacting genetic variations by fish-swarm logic regression. aug: au: Zhang, Xuanping Wang, Jiayin Yang, Aiyuan Yan, Chunxia Zhu, Feng Zhao, Zhongmeng Cao, Zhi affil: Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China. sug: subj: Algorithms Genetic Techniques Genetics Computer Simulation Human Logistic Regression Mutation Polymorphism, Genetic ab: Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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