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Identification and Analysis of Driver Missense Mutations Using Rotation Forest with Feature Selection
Identifying cancer-associated mutations (driver mutations) is critical for understanding the cellular function of cancer genome that leads to activation of oncogenes or inactivation of tumor suppressor genes. Many approaches are proposed which use supervised machine learning techniques for predictio...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Hindawi Publishing Corporation
2014
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4163459/ https://www.ncbi.nlm.nih.gov/pubmed/25250338 http://dx.doi.org/10.1155/2014/905951 |