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Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations.
| Content Provider | Europe PMC |
|---|---|
| Author | Chen, Yukun Sun, Jingchun Huang, Liang-Chin Xu, Hua Zhao, Zhongming |
| Copyright Year | 2015 |
| Abstract | 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. |
| ISSN | 23146133 |
| Journal | Biomed Research International |
| Volume Number | 2015 |
| PubMed Central reference number | PMC4619847 |
| PubMed reference number | 26539502 |
| e-ISSN | 23146141 |
| DOI | 10.1155/2015/491502 |
| Language | English |
| Publisher | Hindawi |
| Publisher Date | 2015-10-11 |
| Access Restriction | Open |
| Rights License | This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2015 Yukun Chen et al. |
| Content Type | Text |
| Resource Type | Article |
| Subject | Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology |
