A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology.
Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in wh...
| Publicado en: | BioMed Research International Vol. 2013; pp. 432375 - 432376 |
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| Autores principales: | , , , |
| Formato: | 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=103995883&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103995883 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: 103995883 2012431374 NLM24228248 PMC3818807 103995883 ppf: 432375 ppct: 1 formats: fmt: @attributes: type: P tig: atl: A review for detecting gene-gene interactions using machine learning methods in genetic epidemiology. aug: au: Koo, Ching Lee Liew, Mei Jing Mohamad, Mohd Saberi Salleh, Abdul Hakim Mohamed affil: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia. sug: subj: Artificial Intelligence Genes Neural Networks (Computer) Algorithms Bioinformatics Epidemiology, Molecular Polymorphism, Genetic ab: Recently, the greatest statistical computational challenge in genetic epidemiology is to identify and characterize the genes that interact with other genes and environment factors that bring the effect on complex multifactorial disease. These gene-gene interactions are also denoted as epitasis in which this phenomenon cannot be solved by traditional statistical method due to the high dimensionality of the data and the occurrence of multiple polymorphism. Hence, there are several machine learning methods to solve such problems by identifying such susceptibility gene which are neural networks (NNs), support vector machine (SVM), and random forests (RFs) in such common and multifactorial disease. This paper gives an overview on machine learning methods, describing the methodology of each machine learning methods and its application in detecting gene-gene and gene-environment interactions. Lastly, this paper discussed each machine learning method and presents the strengths and weaknesses of each machine learning method in detecting gene-gene interactions in complex human disease. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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