scholarly journals Erratum: Comprehensive identification of mutational cancer driver genes across 12 tumor types

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
David Tamborero ◽  
Abel Gonzalez-Perez ◽  
Christian Perez-Llamas ◽  
Jordi Deu-Pons ◽  
Cyriac Kandoth ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Tumor Types

scholarly journals Comprehensive identification of mutational cancer driver genes across 12 tumor types

2013 ◽  
Vol 3 (1) ◽  
Author(s):  
David Tamborero ◽  
Abel Gonzalez-Perez ◽  
Christian Perez-Llamas ◽  
Jordi Deu-Pons ◽  
Cyriac Kandoth ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Tumor Types

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals Module Analysis Captures Pancancer Genetically and Epigenetically Deregulated Cancer Driver Genes for Smoking and Antiviral Response

EBioMedicine ◽  
2018 ◽  
Vol 27 ◽  
pp. 156-166 ◽  
Author(s):  
Magali Champion ◽  
Kevin Brennan ◽  
Tom Croonenborghs ◽  
Andrew J. Gentles ◽  
Nathalie Pochet ◽  
...  
Keyword(s):  
Antiviral Response ◽  
Driver Genes ◽  
Cancer Driver ◽  
Module Analysis ◽  
Cancer Driver Genes

Sex disparities in head & neck cancer driver genes: An analysis of the TCGA dataset

Oral Oncology ◽  
2020 ◽  
Vol 104 ◽  
pp. 104614 ◽  
Author(s):  
Neil Mundi ◽  
Farhad Ghasemi ◽  
Peter Y.F. Zeng ◽  
Stephenie D. Prokopec ◽  
Krupal Patel ◽  
...  
Keyword(s):  
Neck Cancer ◽  
Head Neck Cancer ◽  
Driver Genes ◽  
Cancer Driver ◽  
Sex Disparities ◽  
Cancer Driver Genes ◽  
Tcga Dataset ◽  
Head Neck

scholarly journals Comprehensive Characterization of Cancer Driver Genes and Mutations

Cell ◽  
2018 ◽  
Vol 174 (4) ◽  
pp. 1034-1035 ◽  
Author(s):  
Matthew H. Bailey ◽  
Collin Tokheim ◽  
Eduard Porta-Pardo ◽  
Sohini Sengupta ◽  
Denis Bertrand ◽  
...  
Keyword(s):  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes ◽  
Comprehensive Characterization
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