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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2014-01-01
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| Series: | BioMed Research International |
| Online Access: | http://dx.doi.org/10.1155/2014/905951 |