Ontology highlight
ABSTRACT:
SUBMITTER: Du X
PROVIDER: S-EPMC4163459 | biostudies-literature | 2014
REPOSITORIES: biostudies-literature
BioMed research international 20140827
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 prediction with features obtained by some databases. However, often we do not know which features are important for driver mutations prediction. In this study, we propose a novel feature selection method (call ...[more]