ArticleGenome medicine2014
DawnRank: discovering personalized driver genes in cancer.
Article in Genome medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers, 1 of them a synthesis that pooled it.
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
131 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.PloS one · 2022Pooled it
- Integrated Analysis of RNA and DNA from the Phase III Trial CALGB 40601 Identifies Predictors of Response to Trastuzumab-Based Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2018Trial
- Integrated RNA and DNA sequencing reveals early drivers of metastatic breast cancer.The Journal of clinical investigation · 2018Trial
- Disentangling Heterogeneous Molecular Networks for Multi-Omics-Driven Cancer Driver Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A Novel Cancer Driver Genes Identification Method Based on Self-Supervised Dual Masked Graph Autoencoder.Interdisciplinary sciences, computational life sciences · 2026Article
- 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
- Multiplex networks-based directed graph neural network for cancer driver gene identification.PLoS computational biology · 2026Article
- M-GNN: A Topology-Enhanced Multi-Modal Graph Neural Network for Cancer Driver Gene Prediction.Metabolites · 2026Article
- PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell data.PLoS computational biology · 2026Article
- Molecular characterization of the A52 murine hepatocellular carcinoma cell line.Animal models and experimental medicine · 2026Article
- NetG2P: Network-based genotype-to-phenotype transformation identifies key signaling crosstalk for prognosis in pan-cancer study.BMC biology · 2026Article
- CNAttention: an attention-based deep multiple-instance method for uncovering copy number aberration signatures across cancers.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
- 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
- A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.Genes · 2025Review
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
71 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large-scale cancer genomic studies have revealed that the genetic heterogeneity of the same type of cancer is greater than previously thought. A key question in cancer genomics is the identification of driver genes. Although existing methods have identified many common drivers, it remains challenging to predict personalized drivers to assess rare and even patient-specific mutations. We developed a new algorithm called DawnRank to directly prioritize altered genes on a single patient level. Applications to TCGA datasets demonstrated the effectiveness of our method. We believe DawnRank complements existing driver identification methods and will help us discover personalized causal mutations that would otherwise be obscured by tumor heterogeneity. Source code can be accessed at http://bioen-compbio.bioen.illinois.edu/DawnRank/.
Identifiers
What OpenQuestion holds
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.