ArticleCell reports2018
Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.
Article in Cell reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.
What it found
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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
89 citing papers in PubMed.
- Single-cell multimodal profiling of pan-cancer cell lines uncovers gene regulatory principles underlying intrinsic cell states and environmental features.Nature communications · 2026Article
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- Identification of Critical Genes Related to Breast Cancer with Brain Metastasis Through Bioinformatics Analysis.Current medicinal chemistry · 2026Article
- Expression and functional diversity of the glucagon-like peptide-1 receptor across tumor types.Discover oncology · 2025Article
- Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives.Cells · 2025Review
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- Transcriptional patterns of cancer-related genes in primary and metastatic tumours revealed by machine learning.BMC biology · 2025Article
- Review
- Identification of RASL11A as a gene conferring radiosensitivity in glioblastoma.Journal of neuro-oncology · 2025Article
- iModEst: disentangling -omic impacts on gene expression variation across genes and tissues.NAR genomics and bioinformatics · 2025Article
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- Pan-cancer Comprehensive Analysis Identified EGFR as a Potential Biomarker for Multiple Tumor Types.Applied biochemistry and biotechnology · 2025Article
- Reproducible processing of TCGA regulatory networks.GigaScience · 2025Article
- PELI1 in human cancers: a pan-cancer exploration of its molecular function, clinical significance, and immunomodulatory roles.Frontiers in immunology · 2025Article
- A Review on the Applications of Artificial Intelligence for Diagnosing and Treating Cervical Cancer.International journal of women's health · 2025Review
- Best holdout assessment is sufficient for cancer transcriptomic model selection.Patterns (New York, N.Y.) · 2024Article
- Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.NAR genomics and bioinformatics · 2024Article
- Bayesian unsupervised clustering identifies clinically relevant osteosarcoma subtypes.Briefings in bioinformatics · 2024Article
- The prognostic implications and oncogenic role of NSUN5 in clear cell renal cell carcinoma.Clinical and experimental medicine · 2024Article
29 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.