Performance Comparison of Deep Learning Autoencoders for Cancer Subtype Detection Using Multi-Omics Data.

ABSTRACT: A heterogeneous disease such as cancer is activated through multiple pathways and different perturbations. Depending upon the activated pathway(s), the survival of the patients varies significantly and shows different efficacy to various drugs. Therefore, cancer subtype detection using genomics level data is a significant research problem. Subtype detection is often a complex problem, and in most cases, needs multi-omics data fusion to achieve accurate subtyping. Different data fusion and subtyping approaches have been proposed over the years, such as kernel-based fusion, matrix factorization, and deep learning autoencoders. In this paper, we compared the performance of different deep learning autoencoders for cancer subtype detection. We performed cancer subtype detection on four differen

SUBMITTER: Franco EF 

PROVIDER: S-EPMC8122584 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature