ArticleNucleic acids research2020
DriverDBv3: a multi-omics database for cancer driver gene research.
Article in Nucleic acids research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.
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131 citing papers in PubMed.
- Proteogenomic characterization of pulmonary large-cell neuroendocrine carcinoma identifies molecular features and therapeutic strategy.Science advances · 2026Article
- Multi-omics profiling unveils biological and clinical insights into pulmonary sarcomatoid carcinoma.Cell reports. Medicine · 2026Article
- An organoid-guided platform for ovarian cancer: enabling prediction of patients' chemotherapy response.Journal of ovarian research · 2026Article
- The cumulative impact of passenger mutations on cancer development.Nature communications · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Establishment of a xenograft model of endometriosis-associated fibrosis using human immortalized endometrial stromal cells overexpressing HOXC8.Scientific reports · 2026Article
- Comprehensive review of publicly accessible databases for gastrointestinal cancer and future challenges in database construction.Discover oncology · 2026Review
- Intratumoral bacteria are immunosuppressive and promote immunotherapy resistance in head and neck squamous cell carcinoma.Nature cancer · 2026Article
- Integrative multi-omics and preclinical analyses identify miR-4776-5p as a prognostic radiosensitizer for patients undergoing radiotherapy for head-and-neck cancer.Discover oncology · 2025Article
- A core driver gene set identified based on geMER reveals its potential driver mechanism in pan-cancer.NPJ precision oncology · 2025Article
- Network-based approach identifies key genes associated with tumor heterogeneity in HPV positive and negative head and neck cancer patients.Scientific reports · 2025Article
- Distinctive chromosomal, mutational and transcriptional profiling in colon versus rectal cancers.Journal of translational medicine · 2025Article
- Transcriptome-wide decoding the roles of aberrant splicing in melanoma MAPK-targeted resistance evolution.EMBO reports · 2025Article
- DriverOmicsNet: an integrated graph convolutional network for multi-omics exploration of cancer driver genes.Briefings in bioinformatics · 2025Article
- A 3D Genome Atlas of Genetic Variants and Their Pathological Effects in Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Memory-promoting function of miR-379-5p attenuates CD8Journal for immunotherapy of cancer · 2025Article
- Cancer gene identification through integrating causal prompting large language model with omics data-driven causal inference.Briefings in bioinformatics · 2025Article
- Investigation of the genome-wide signatures underlying the micropapillary carcinoma components in colorectal cancers.Genes & diseases · 2025Article
- BUB1, miR-495-3p, and E2F1/E2F8 axis is associated with poor prognosis of breast cancer patients and infiltration of Th2 cells in the tumor microenvironment.Cancer biomarkers : section A of Disease markers · 2025Article
- Annexin A's Life in Pan-Cancer: Especially in Glioma Immune Cells.Neuromolecular medicine · 2025Review
71 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
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Abstract
An integrative multi-omics database is needed urgently, because focusing only on analysis of one-dimensional data falls far short of providing an understanding of cancer. Previously, we presented DriverDB, a cancer driver gene database that applies published bioinformatics algorithms to identify driver genes/mutations. The updated DriverDBv3 database (http://ngs.ym.edu.tw/driverdb) is designed to interpret cancer omics' sophisticated information with concise data visualization. To offer diverse insights into molecular dysregulation/dysfunction events, we incorporated computational tools to define CNV and methylation drivers. Further, four new features, CNV, Methylation, Survival, and miRNA, allow users to explore the relations from two perspectives in the 'Cancer' and 'Gene' sections. The 'Survival' panel offers not only significant survival genes, but gene pairs synergistic effects determine. A fresh function, 'Survival Analysis' in 'Customized-analysis,' allows users to investigate the co-occurring events in user-defined gene(s) by mutation status or by expression in a specific patient group. Moreover, we redesigned the web interface and provided interactive figures to interpret cancer omics' sophisticated information, and also constructed a Summary panel in the 'Cancer' and 'Gene' sections to visualize the features on multi-omics levels concisely. DriverDBv3 seeks to improve the study of integrative cancer omics data by identifying driver genes and contributes to cancer biology.
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