ArticleNucleic acids research2019
DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed.
- Uncertainty-aware graph structure optimization with ensemble learning for enhanced cancer gene identification.Cell reports methods · 2026Article
- A Novel Cancer Driver Genes Identification Method Based on Self-Supervised Dual Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- Frequent EPHA2 receptor mutations in cholangiocarcinoma disrupt receptor forward signaling supporting a tumor suppressor role.Biochimica et biophysica acta. Molecular cell research · 2026Article
- Integrating Multi-Source Directed Gene Networks and Multi-Omics Data to Identify Cancer Driver Genes Based on Graph Neural Networks.International journal of molecular sciences · 2025Article
- Pangenome-Informed Language Models for Synthetic Genome Sequence Generation.bioRxiv : the preprint server for biology · 2025Article
- MLGCN-Driver: a cancer driver gene identification method based on multi-layer graph convolutional neural network.BMC bioinformatics · 2025Article
- A core driver gene set identified based on geMER reveals its potential driver mechanism in pan-cancer.NPJ precision oncology · 2025Article
- Distinctive chromosomal, mutational and transcriptional profiling in colon versus rectal cancers.Journal of translational medicine · 2025Article
- A comprehensive database for identifying and interpreting ctDNA driver genes and variants in cancer.Scientific data · 2025Article
- Gene mutations and differentiation in laryngeal and pharyngeal squamous cell carcinoma.Discover oncology · 2025Article
- Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.PLoS computational biology · 2025Article
- Cancer gene identification through integrating causal prompting large language model with omics data-driven causal inference.Briefings in bioinformatics · 2025Article
- Decoding the functional impact of the cancer genome through protein-protein interactions.Nature reviews. Cancer · 2025Review
- Identifying somatic driver mutations in cancer with a language model of the human genome.Computational and structural biotechnology journal · 2025Article
- Multimodal multiobjective optimization with structural network control principles to optimize personalized drug targets for drug discovery of individual patients.Briefings in bioinformatics · 2024Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.PLoS computational biology · 2024Article
- Article
- MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.Bioinformatics (Oxford, England) · 2024Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predicted from these computational tools lack consistency and are prone to false positives. Here, we developed an approach (DriverML) integrating Rao's score test and supervised machine learning to identify cancer driver genes. The weight parameters in the score statistics quantified the functional impacts of mutations on the protein. To obtain optimized weight parameters, the score statistics of prior driver genes were maximized on pan-cancer training data. We conducted rigorous and unbiased benchmark analysis and comparisons of DriverML with 20 other existing tools in 31 independent datasets from The Cancer Genome Atlas (TCGA). Our comprehensive evaluations demonstrated that DriverML was robust and powerful among various datasets and outperformed the other tools with a better balance of precision and sensitivity. In vitro cell-based assays further proved the validity of the DriverML prediction of novel driver genes. In summary, DriverML uses an innovative, machine learning-based approach to prioritize cancer driver genes and provides dramatic improvements over currently existing methods. Its source code is available at https://github.com/HelloYiHan/DriverML.
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