ReviewGenome medicine2014
Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine.
Review in Genome medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
113 citing papers in PubMed, 1 synthesis or guideline pooled it, 232 citations in OpenAlex.
- Genome-wide meta-analysis across East Asian and European populations provides insights into the molecular basis of lung cancer.Genome medicine · 2026Pooled it
- Evaluation of two highly-multiplexed custom panels for massively parallel semiconductor sequencing on paraffin DNA.PloS one · 2015Trial
- A novel causality-based method for identifying drivers of breast cancer progression.Bioinformatics (Oxford, England) · 2026Article
- EPIC: Event Prototyping via Information Constrained graph learning for personalized cancer driver gene prediction.Bioinformatics (Oxford, England) · 2026Article
- Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data.Biomolecules · 2025Article
- Transcriptome-wide root causal inference.PLoS computational biology · 2025Article
- Bacterial two-hybrid systems evolved: innovations for protein-protein interaction research.Journal of bacteriology · 2025Review
- [Application of the variant allele frequency of myeloid-associated gene mutations in myelodysplastic syndrome].Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi · 2025Review
- Multiomic quantification of the KRAS mutation dosage improves the preoperative prediction of survival and recurrence in patients with pancreatic ductal adenocarcinoma.Experimental & molecular medicine · 2025Article
- Quantifying Metagenomic Strain Associations from Microbiomes with Anpan.bioRxiv : the preprint server for biology · 2025Article
- Integrating computational insights in gold nanoparticle-mediated drug delivery: enhancing efficacy and precision.Frontiers in medical technology · 2025Review
- Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes.Cell reports · 2024Article
- Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers.Scientific reports · 2024Article
- ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.PLoS computational biology · 2024Article
- Article
- A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer.bioRxiv : the preprint server for biology · 2024Article
- A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.PLoS computational biology · 2024Article
- Moving toward precision medicine to predict drug sensitivity in patients with metastatic breast cancer.ESMO open · 2024Review
- Review
- Genomic profiling of thymoma using a targeted high-throughput approach.Archives of medical science : AMS · 2024Article
53 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
4 authors at 1 institution in 1 country.
Funding
Abstract
High-throughput DNA sequencing is revolutionizing the study of cancer and enabling the measurement of the somatic mutations that drive cancer development. However, the resulting sequencing datasets are large and complex, obscuring the clinically important mutations in a background of errors, noise, and random mutations. Here, we review computational approaches to identify somatic mutations in cancer genome sequences and to distinguish the driver mutations that are responsible for cancer from random, passenger mutations. First, we describe approaches to detect somatic mutations from high-throughput DNA sequencing data, particularly for tumor samples that comprise heterogeneous populations of cells. Next, we review computational approaches that aim to predict driver mutations according to their frequency of occurrence in a cohort of samples, or according to their predicted functional impact on protein sequence or structure. Finally, we review techniques to identify recurrent combinations of somatic mutations, including approaches that examine mutations in known pathways or protein-interaction networks, as well as de novo approaches that identify combinations of mutations according to statistical patterns of mutual exclusivity. These techniques, coupled with advances in high-throughput DNA sequencing, are enabling precision medicine approaches to the diagnosis and treatment of cancer.
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.