A pan-cancer somatic mutation embedding using autoencoders.

ABSTRACT: BACKGROUND:Next generation sequencing instruments are providing new opportunities for comprehensive analyses of cancer genomes. The increasing availability of tumor data allows to research the complexity of cancer disease with machine learning methods. The large available repositories of high dimensional tumor samples characterised with germline and somatic mutation data requires advance computational modelling for data interpretation. In this work, we propose to analyze this complex data with neural network learning, a methodology that made impressive advances in image and natural language processing. RESULTS:Here we present a tumor mutation profile analysis pipeline based on an autoencoder model, which is used to discover better representations of lower dimensionality from large somatic

SUBMITTER: Palazzo M 

PROVIDER: S-EPMC6907172 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature