Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis.

ABSTRACT:

Background

Breast cancer is the most prevalent and among the most deadly cancers in females. Patients with breast cancer have highly variable survival lengths, indicating a need to identify prognostic biomarkers for personalized diagnosis and treatment. With the development of new technologies such as next-generation sequencing, multi-omics information are becoming available for a more thorough evaluation of a patient's condition. In this study, we aim to improve breast cancer overall survival prediction by integrating multi-omics data (e.g., gene expression, DNA methylation, miRNA expression, and copy number variations (CNVs)).

Methods

Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applyin

SUBMITTER: Tong L 

PROVIDER: S-EPMC7493161 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature