A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data.
Background: In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chron...
| Publicado en: | Journal of Translational Medicine Vol. 7; pp. 81 - 82 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
2009
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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=105233146&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105233146 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14795876 1CW3 jtl: Journal of Translational Medicine issn: 14795876 maglogo: N pubinfo: dt: 2009 vid: 7 pid: 24147 pub: BioMed Central artinfo: ui: 105233146 NLM19772600 2010449633 10.1186/1479-5876-7-81 NLM19772600 PMC2765429 105233146 ppf: 81 ppct: 1 formats: tig: atl: A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data. aug: au: Huang LC Hsu SY Lin E Huang, Lung-Cheng Hsu, Sen-Yen Lin, Eugene affil: Department of Psychiatry, National Taiwan University Hospital Yun-Lin Branch, Taiwan sug: subj: Bioinformatics Methods Fatigue Syndrome, Chronic Classification Fatigue Syndrome, Chronic Disease Susceptibility Genomics Classification Genomics Methods Polymorphism, Genetic Algorithms Probability Decision Trees Human Reproducibility of Results Sensitivity and Specificity ab: Background: In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for the association studies of disease susceptibility. In this work, our goal was to compare computational tools with and without feature selection for predicting chronic fatigue syndrome (CFS) using genetic factors such as single nucleotide polymorphisms (SNPs).Methods: We employed the dataset that was original to the previous study by the CDC Chronic Fatigue Syndrome Research Group. To uncover relationships between CFS and SNPs, we applied three classification algorithms including naive Bayes, the support vector machine algorithm, and the C4.5 decision tree algorithm. Furthermore, we utilized feature selection methods to identify a subset of influential SNPs. One was the hybrid feature selection approach combining the chi-squared and information-gain methods. The other was the wrapper-based feature selection method.Results: The naive Bayes model with the wrapper-based approach performed maximally among predictive models to infer the disease susceptibility dealing with the complex relationship between CFS and SNPs.Conclusion: We demonstrated that our approach is a promising method to assess the associations between CFS and SNPs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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