Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data.

ABSTRACT:

Motivation

Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.

Results

To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC

SUBMITTER: Chen R 

PROVIDER: S-EPMC8215925 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature