Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations.
An accurate classification of human cancer, including its primary site, is important for better understanding of cancer and effective therapeutic strategies development. The available big data of somatic mutations provides us a great opportunity to investigate cancer classification using machine lea...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 10 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
10/11/2015
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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=110561914&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110561914 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/11/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 110561914 10.1155/2015/491502 110561914 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. aug: au: Chen, Yukun Sun, Jingchun Huang, Liang-Chin Xu, Hua Zhao, Zhongming affil: Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37203, USA sug: ab: An accurate classification of human cancer, including its primary site, is important for better understanding of cancer and effective therapeutic strategies development. The available big data of somatic mutations provides us a great opportunity to investigate cancer classification using machine learning. Here, we explored the patterns of 1,760,846 somatic mutations identified from 230,255 cancer patients along with gene function information using support vector machine. Specifically, we performed a multiclass classification experiment over the 17 tumor sites using the gene symbol, somatic mutation, chromosome, and gene functional pathway as predictors for 6,751 subjects. The performance of the baseline using only gene features is 0.57 in accuracy. It was improved to 0.62 when adding the information of mutation and chromosome. Among the predictable primary tumor sites, the prediction of five primary sites (large intestine, liver, skin, pancreas, and lung) could achieve the performance with more than 0.70 in F-measure. The model of the large intestine ranked the first with 0.87 in F-measure. The results demonstrate that the somatic mutation information is useful for prediction of primary tumor sites with machine learning modeling. To our knowledge, this study is the first investigation of the primary sites classification using machine learning and somatic mutation data. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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