Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification.
Cancer is a deadly disease which requires a very complex and costly treatment. Microarray data classification plays an important role in cancer treatment. An efficient gene selection technique to select the more promising genes is necessary for cancer classification. Here, we propose a Two-stage MI-...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 8 |
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| Autores principales: | , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2019
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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=137490033&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137490033 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2019 vid: 43 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137490033 137490033 137490033 10.1007/s10916-019-1372-8 137490033 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification. aug: au: Jansi Rani, M. Devaraj, D. affil: School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar, India sug: subj: Neoplasms Classification Data Analytics Data Mining Genes Sequence Analysis Genetic Algorithms Neoplasms Therapy Genetic Techniques Methods Information Management Gene Expression Image Processing, Computer Assisted Methods Colonic Neoplasms Lung Neoplasms Ovarian Neoplasms ab: Cancer is a deadly disease which requires a very complex and costly treatment. Microarray data classification plays an important role in cancer treatment. An efficient gene selection technique to select the more promising genes is necessary for cancer classification. Here, we propose a Two-stage MI-GA Gene Selection algorithm for selecting informative genes in cancer data classification. In the first stage, Mutual Information based gene selection is applied which selects only the genes that have high information related to the cancer. The genes which have high mutual information value are given as input to the second stage. The Genetic Algorithm based gene selection is applied in the second stage to identify and select the optimal set of genes required for accurate classification. For classification, Support Vector Machine (SVM) is used. The proposed MI-GA gene selection approach is applied to Colon, Lung and Ovarian cancer datasets and the results show that the proposed gene selection approach results in higher classification accuracy compared to the existing methods. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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