ReviewBiophysical reviews2019
Finding cancer driver mutations in the era of big data research.
Review in Biophysical reviews, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
10 citing papers in PubMed.
- Severity of effect considerations regarding the use of mutation as a toxicological endpoint for risk assessment: A report from the 8th International Workshop on Genotoxicity Testing (IWGT).Environmental and molecular mutagenesis · 2025Review
- Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma.Biomedicines · 2022Review
- The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma.Cell death & disease · 2022Article
- Mutational landscape of paired primary and synchronous metastatic lymph node in chemotherapy naive gallbladder cancer.Molecular biology reports · 2022Article
- A model for an undergraduate research experience program in quantitative sciences.Journal of statistics and data science education : an official journal of the of the American Statistical Association · 2022Article
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
- Big data: the elements of good questions, open data, and powerful software.Biophysical reviews · 2019Article
- Precision medicine review: rare driver mutations and their biophysical classification.Biophysical reviews · 2019Review
- The Network of Cancer Genes (NCG): a comprehensive catalogue of known and candidate cancer genes from cancer sequencing screens.Genome biology · 2019Article
- Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations.Frontiers in molecular biosciences · 2019Article
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
Abstract
In the last decade, the costs of genome sequencing have decreased considerably. The commencement of large-scale cancer sequencing projects has enabled cancer genomics to join the big data revolution. One of the challenges still facing cancer genomics research is determining which are the driver mutations in an individual cancer, as these contribute only a small subset of the overall mutation profile of a tumour. Focusing primarily on somatic single nucleotide mutations in this review, we consider both coding and non-coding driver mutations, and discuss how such mutations might be identified from cancer sequencing datasets. We describe some of the tools and database that are available for the annotation of somatic variants and the identification of cancer driver genes. We also address the use of genome-wide variation in mutation load to establish background mutation rates from which to identify driver mutations under positive selection. Finally, we describe the ways in which mutational signatures can act as clues for the identification of cancer drivers, as these mutations may cause, or arise from, certain mutational processes. By defining the molecular changes responsible for driving cancer development, new cancer treatment strategies may be developed or novel preventative measures proposed.
Indexed as
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.