ArticleNature cancer2025
Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.
Article in Nature cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Integrated proteogenomic and metabolomic profiling of acute myeloid leukemias to identify molecular subtypes and associated therapy targets.Nature cancer · 2026Article
- Identification of prognostic biomarkers and immunotherapy response predictors in lung adenocarcinoma: integrative Mendelian randomization and machine learning analysis.Translational lung cancer research · 2026Article
- Discovering proteo-transcriptomic networks via biologically informed heterogeneous graph learning.Nucleic acids research · 2026Article
- CancerHubs Data Explorer: a web application for investigating mutation-enriched protein interaction hubs in human cancers.BioData mining · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- ProteoBoostR: an interactive framework for supervised machine learning in clinical proteomics.Clinical proteomics · 2026Article
- Panorama: a database for the oncogenic evaluation of somatic mutations in pan-cancer.Database : the journal of biological databases and curation · 2026Article
- Network and Gene Set Enrichment Analysis of Adipokine Drivers of Prostate Cancer; Unravelling the Mechanistic Link Between Excess Adiposity and Prostate Cancer Risk.Cancer medicine · 2026Article
- Transcriptome remodelling and changes in growth and cardiometabolic phenotype result following Grb10a knockdown in the early life of the zebrafish.Cellular and molecular life sciences : CMLS · 2025Article
- NAA10 (N-Alpha-Acetyltransferase 10): A Multifunctional Regulator in Development, Disease, and Cancer.Cells · 2025Review
- Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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 functions for understudied cancer-associated proteins. Additionally, applying graph-neural-network-based deep learning to FunMap uncovers drivers with low mutation frequency. This study establishes FunMap as a powerful and unbiased tool for interpreting somatic mutations and understudied proteins, with broad implications for advancing cancer biology and informing therapeutic strategies.
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