DriverSub-SVM: a machine learning approach for cancer subtype classification by integrating patient-specific and global driver genes.

ABSTRACT:

Background

Cancer's complexity and heterogeneity pose significant challenges for personalized treatment. Accurate classification of patients into molecular subtypes is critical for targeted therapy and improved outcomes. However, existing methods often fail to simultaneously capture inter-patient heterogeneity and shared molecular patterns in driver gene profiles.

Results

To address this limitation, we propose DriverSub-SVM, a novel framework for interpretable cancer subtype classification that integrates patient-specific and cohort-wide driver gene information. Our method first models the bidirectional influence between mutated and dysregulated genes via a random walk on a functional interaction network. It then applies Bayesian Personalized Ranking (BPR) to infer personali

SUBMITTER: Song J 

PROVIDER: S-EPMC12751776 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature