Comparison of DNA methylation based classification models for precision diagnostics of central nervous system tumors.

ABSTRACT: As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children's Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models' performance was rigorously validated against 2054 samples from two independent cohorts. In addition to classic metrics of model performance, we compared the robustness of the three models to reduced tumor purity, a critical consideration in the clinical utility of such classifiers. Our findings revealed that the NN model exhibited the highest accuracy and maintained a balance between precision and recall. The NN mode

SUBMITTER: Tran QT 

PROVIDER: S-EPMC11447224 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature