Colorectal cancer subtype identification from differential gene expression levels using minimalist deep learning.

ABSTRACT:

Background

Cancer molecular subtyping plays a critical role in individualized patient treatment. In previous studies, high-throughput gene expression signature-based methods have been proposed to identify cancer subtypes. Unfortunately, the existing ones suffer from the curse of dimensionality, data sparsity, and computational deficiency.

Methods

To address those problems, we propose a computational framework for colorectal cancer subtyping without any exploitation in model complexity and generality. A supervised learning framework based on deep learning (DeepCSD) is proposed to identify cancer subtypes. Specifically, based on the differentially expressed genes under cancer consensus molecular subtyping, we design a minimalist feed-forward neural network to capture the disti

SUBMITTER: Li S 

PROVIDER: S-EPMC9034628 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature