DeepOmix: A scalable and interpretable multi-omics deep learning framework and application in cancer survival analysis.

ABSTRACT: Integrative analysis of multi-omics data can elucidate valuable insights into complex molecular mechanisms for various diseases. However, due to their different modalities and high dimension, utilizing and integrating different types of omics data suffers from great challenges. There is an urgent need to develop a powerful method to improve survival prediction and detect functional gene modules from multi-omics data. To deal with these problems, we present DeepOmix (a scalable and interpretable multi-Omics Deep learning framework and application in cancer survival analysis), a flexible, scalable, and interpretable method for extracting relationships between the clinical survival time and multi-omics data based on a deep learning framework. DeepOmix enables the non-linear combination of var

SUBMITTER: Zhao L 

PROVIDER: S-EPMC8131983 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature