ArticleBioinformatics (Oxford, England)2020
PRODIGY: personalized prioritization of driver genes.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.PloS one · 2022Pooled it
- EPIC: Event Prototyping via Information Constrained graph learning for personalized cancer driver gene prediction.Bioinformatics (Oxford, England) · 2026Article
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- GRAFT: a graph-aware fusion transformer for cancer driver gene prediction.Briefings in bioinformatics · 2026Article
- A new algorithm Precision OncoPanels (PrOPs) identifies short individualized actionable panels that can guide cancer treatment: a pan-cancer analysis of TCGA cohorts.NAR genomics and bioinformatics · 2025Article
- Distinctive chromosomal, mutational and transcriptional profiling in colon versus rectal cancers.Journal of translational medicine · 2025Article
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.PLoS computational biology · 2025Article
- GETgene-AI: a framework for prioritizing actionable cancer drug targets.Frontiers in systems biology · 2025Article
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Mapping the functional network of human cancer through machine learning and pan-cancer proteogenomics.Nature cancer · 2025Article
- Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.Briefings in bioinformatics · 2024Article
- A feature extraction framework for discovering pan-cancer driver genes based on multi-omics data.Quantitative biology (Beijing, China) · 2024Article
- A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.PLoS computational biology · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.BMC bioinformatics · 2024Article
- Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.Molecular oncology · 2023Article
- Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.Journal of molecular evolution · 2023Review
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- Interrogating the Human Diplome: Computational Methods, Emerging Applications, and Challenges.Methods in molecular biology (Clifton, N.J.) · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationEvolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, whereas most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification but patient-specific driver gene identification remains a challenge.
methodsWe developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize-collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways.
resultsIn testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on network centrality measures. Our results pinpoint the pleiotropic effect of driver genes and show that Prodigy is capable of identifying even very rare drivers. Hence, Prodigy takes a step further toward personalized medicine and treatment. AVAILABILITY AND IMPLEMENTATION: The Prodigy R package is available at: https://github.com/Shamir-Lab/PRODIGY. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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