HallmarkGraph: a cancer hallmark informed graph neural network for classifying hierarchical tumor subtypes.

ABSTRACT:

Motivation

Accurate tumor subtype diagnosis is crucial for precision oncology, yet current methodologies face significant challenges. These include balancing model accuracy with interpretability and the high costs of generating multi-omics data in clinical settings. Moreover, there is a lack of validated models capable of classifying hierarchical tumor subtypes across a comprehensive pan-cancer cohort.

Results

We present a graph neural network, HallmarkGraph, the first biologically informed model developed to classify hierarchical tumor subtypes in human cancer. Inspired by cancer hallmarks, the model's architecture integrates transcriptome profiles and gene regulatory interactions to perform multi-label classification. We evaluate the model on a comprehensive pan-cancer coh

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC12401579 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature