Neighborhood rough set reduction-based gene selection and prioritization for gene expression profile analysis and molecular cancer classification.
Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cance...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 12p - 13 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2010
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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=105082250&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105082250 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2010 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105082250 105082250 2010748605 NLM20625410 PMC2896865 105082250 ppf: 12p ppct: 1 formats: fmt: @attributes: type: P tig: atl: Neighborhood rough set reduction-based gene selection and prioritization for gene expression profile analysis and molecular cancer classification. aug: au: Hou M Wang S Li X Lei Y affil: Intelligent Computing Laboratory, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China. sug: subj: Gene Expression Neoplasms Classification Neoplasms Diagnosis Biological Markers Funding Source Human Kruskal-Wallis Test Oncogenes T-Tests ab: Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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