Xin Lai

Tumor subtypes form a hierarchy running from primary cancer type down to molecular subtype, yet most classifiers flatten the labels and ignore the biological constraints between levels. HHRM combines cancer hallmark gene networks with recursive modules to predict level by level across 11,476 pan-cancer samples covering 26 primary cancers, 405 subtypes and up to eight levels, reaching 0.7924 accuracy in local output mode, above the hierarchical models it was compared with. Retrained on an independent set of 1,608 breast cancer samples, it exceeded 0.86 accuracy on both test and validation data.

Tumor subtypes form a hierarchy running from primary cancer type down to molecular subtype, yet most classifiers flatten the labels and ignore the biological constraints between levels. HHRM combines cancer hallmark gene networks with recursive modules to predict level by level across 11,476 pan-cancer samples covering 26 primary cancers, 405 subtypes and up to eight levels, reaching 0.7924 accuracy in local output mode, above the hierarchical models it was compared with. Retrained on an independent set of 1,608 breast cancer samples, it exceeded 0.86 accuracy on both test and validation data.