Knowledge Generation with Rule Induction in Cancer Omics.

ABSTRACT: The explosion of omics data availability in cancer research has boosted the knowledge of the molecular basis of cancer, although the strategies for its definitive resolution are still not well established. The complexity of cancer biology, given by the high heterogeneity of cancer cells, leads to the development of pharmacoresistance for many patients, hampering the efficacy of therapeutic approaches. Machine learning techniques have been implemented to extract knowledge from cancer omics data in order to address fundamental issues in cancer research, as well as the classification of clinically relevant sub-groups of patients and for the identification of biomarkers for disease risk and prognosis. Rule induction algorithms are a group of pattern discovery approaches that represents discove

SUBMITTER: Scala G 

PROVIDER: S-EPMC6981587 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature