Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors.

ABSTRACT:

Background

Precision medicine for cancer treatment relies on an accurate pathological diagnosis. The number of known tumor classes has increased rapidly, and reliance on traditional methods of histopathologic classification alone has become unfeasible. To help reduce variability, validation costs, and standardize the histopathological diagnostic process, supervised machine learning models using DNA-methylation data have been developed for tumor classification. These methods require large labeled training data sets to obtain clinically acceptable classification accuracy. While there is abundant unlabeled epigenetic data across multiple databases, labeling pathology data for machine learning models is time-consuming and resource-intensive, especially for rare tumor types. Semi-superv

SUBMITTER: Tran QT 

PROVIDER: S-EPMC9178802 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature