Molecular Subtyping of Cancer Based on Distinguishing Co-Expression Modules and Machine Learning.

ABSTRACT: Molecular subtyping of cancer is recognized as a critical and challenging step towards individualized therapy. Most existing computational methods solve this problem via multi-classification of gene-expressions of cancer samples. Although these methods, especially deep learning, perform well in data classification, they usually require large amounts of data for model training and have limitations in interpretability. Besides, as cancer is a complex systemic disease, the phenotypic difference between cancer samples can hardly be fully understood by only analyzing single molecules, and differential expression-based molecular subtyping methods are reportedly not conserved. To address the above issues, we present here a new framework for molecular subtyping of cancer through identifying

SUBMITTER: Sun P 

PROVIDER: S-EPMC9108363 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature