CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network.

ABSTRACT: With recent advances in DNA sequencing technologies, fast acquisition of large-scale genomic data has become commonplace. For cancer studies, in particular, there is an increasing need for the classification of cancer type based on somatic alterations detected from sequencing analyses. However, the ever-increasing size and complexity of the data make the classification task extremely challenging. In this study, we evaluate the contributions of various input features, such as mutation profiles, mutation rates, mutation spectra and signatures, and somatic copy number alterations that can be derived from genomic data, and further utilize them for accurate cancer type classification. We introduce a novel ensemble of machine learning classifiers, called CPEM (Cancer Predictor using an Ensemble

SUBMITTER: Lee K 

PROVIDER: S-EPMC6858312 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature