Multi-omics driven computational framework for cancer molecular subtype classification.

ABSTRACT: Cancer molecular subtype classification is an essential component of precision oncology which provides insights into cancer prognosis and guides targeted therapy. Despite the growing applications of AI for cancer molecular subtype classification, challenges persist due to non-standardized dataset configurations, diverse omics modalities, and inconsistent evaluation measures. These issues limit the comparability, reproducibility, and generalizability of AI classifiers across different cancers and hinder the development of robust and accurate AI-driven tools. This study performs comparative analyses of 35 unique AI classifiers across 153 datasets, covering 8 omics modalities and 20 different cancers. Particularly, it investigates 6 different research questions, and based on comprehensive per

SUBMITTER: Abbasi AF 

PROVIDER: S-EPMC12717050 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature