ArticleNature methods2017
Comparison of algorithms for the detection of cancer drivers at subgene resolution.
Article in Nature methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 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.
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Who cites it
49 citing papers in PubMed, 84 citations in OpenAlex.
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- Global analysis of actionable genomic alterations in thyroid cancer and precision-based pharmacogenomic strategies.Frontiers in pharmacology · 2025Article
- Worldwide analysis of actionable genomic alterations in lung cancer and targeted pharmacogenomic strategies.Heliyon · 2024Article
- CIBRA identifies genomic alterations with a system-wide impact on tumor biology.Bioinformatics (Oxford, England) · 2024Article
- Gastric cancer actionable genomic alterations across diverse populations worldwide and pharmacogenomics strategies based on precision oncology.Frontiers in pharmacology · 2024Article
- Assessing the validity of driver gene identification tools for targeted genome sequencing data.Bioinformatics advances · 2024Article
- Cancer driver mutations: predictions and reality.Trends in molecular medicine · 2023Review
- Cancer as an infective disease: the role of EVs in tumorigenesis.Molecular oncology · 2023Review
- Ectopic expression of meiotic cohesin generates chromosome instability in cancer cell line.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Cancer-driving mutations are enriched in genic regions intolerant to germline variation.Science advances · 2022Article
- Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource.Genome biology · 2022Review
- Integration of artificial intelligence and precision oncology in Latin America.Frontiers in medical technology · 2022Review
- Integrative study reveals the prognostic and immunotherapeutic value of CD274 and PDCD1LG2 in pan-cancer.Frontiers in genetics · 2022Article
- A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.Genome research · 2022Article
- The interplay of SARS-CoV-2 evolution and constraints imposed by the structure and functionality of its proteins.PLoS computational biology · 2021Article
- Personalized oncology and BRAFJournal of cancer research and clinical oncology · 2021Article
- A New Insight for the Identification of Oncogenic Variants in Breast and Prostate Cancers in Diverse Human Populations, With a Focus on Latinos.Frontiers in pharmacology · 2021Article
- A compendium of mutational cancer driver genes.Nature reviews. Cancer · 2020Review
- The interplay of SARS-CoV-2 evolution and constraints imposed by the structure and functionality of its proteins.bioRxiv : the preprint server for biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.
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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 Socratic graph.