Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.

ABSTRACT: Large-scale omics profiling has uncovered a vast array of somatic mutations and cancer-associated proteins, posing substantial challenges for their functional interpretation. Here we present a network-based approach centered on FunMap, a pan-cancer functional network constructed using supervised machine learning on extensive proteomics and RNA sequencing data from 1,194 individuals spanning 11 cancer types. Comprising 10,525 protein-coding genes, FunMap connects functionally associated genes with unprecedented precision, surpassing traditional protein-protein interaction maps. Network analysis identifies functional protein modules, reveals a hierarchical structure linked to cancer hallmarks and clinical phenotypes, provides deeper insights into established cancer drivers and predicts funct

SUBMITTER: Shi Z 

PROVIDER: S-EPMC12036749 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature