Simulated data used in the driverMAPS paper: Zhao, S. et al. Model-based analysis of positive selection significantly expands the list of cancer driver genes, including RNA methyltransferases. bioRxiv (2018). at http://biorxiv.org/content/early/2018/07/12/366823.abstract The file contains mutations simulated for 100, 200, 400, 600, 800, 1000, 1500,2000 samples with mutation rates obtained from the UCS cohort from TCGA. See the above reference for details of simulation procedures. version: simulation_2018080
Dataset contains results of multiply parallel calculations using the tugHall simulator. Output data ...
Snapshots from the MutaGene server show the results of analysis of EGFR gene with a Pan-cancer model...
<p>The true positive rate (TPR, row 1), false discovery rate (FDR, row 2) and false positive rate (F...
Simulated data used in the driverMAPS paper: Zhao, S. et al. Model-based analysis of positive sele...
Motivation: Identifying and prioritizing somatic mutations is an important and challenging area of c...
Copyright © 2014 Hai-Tao Li et al. This is an open access article distributed under the Creative Com...
© 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer devel...
© 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer devel...
Datasets containing training and validation data for D2Deep predictor: common_variants: commona v...
The notion that DNA changes could drive the growth of cancer was first speculated more than a centur...
Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation i...
The past decade’s progress in next generation sequencing has drastically decreased the price of whol...
Summary: Large-scale cancer sequencing studies of patient cohorts have statistically implicated many...
<p>The true positive rate (TPR, row 1), false discovery rate (FDR, row 2) and false positive rate (F...
<p>(<b>A</b>) A simulated tumor. Different colors represent different clones. White rectangles label...
Dataset contains results of multiply parallel calculations using the tugHall simulator. Output data ...
Snapshots from the MutaGene server show the results of analysis of EGFR gene with a Pan-cancer model...
<p>The true positive rate (TPR, row 1), false discovery rate (FDR, row 2) and false positive rate (F...
Simulated data used in the driverMAPS paper: Zhao, S. et al. Model-based analysis of positive sele...
Motivation: Identifying and prioritizing somatic mutations is an important and challenging area of c...
Copyright © 2014 Hai-Tao Li et al. This is an open access article distributed under the Creative Com...
© 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer devel...
© 2018 The Author(s). Motivation Understanding the mutational processes that act during cancer devel...
Datasets containing training and validation data for D2Deep predictor: common_variants: commona v...
The notion that DNA changes could drive the growth of cancer was first speculated more than a centur...
Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation i...
The past decade’s progress in next generation sequencing has drastically decreased the price of whol...
Summary: Large-scale cancer sequencing studies of patient cohorts have statistically implicated many...
<p>The true positive rate (TPR, row 1), false discovery rate (FDR, row 2) and false positive rate (F...
<p>(<b>A</b>) A simulated tumor. Different colors represent different clones. White rectangles label...
Dataset contains results of multiply parallel calculations using the tugHall simulator. Output data ...
Snapshots from the MutaGene server show the results of analysis of EGFR gene with a Pan-cancer model...
<p>The true positive rate (TPR, row 1), false discovery rate (FDR, row 2) and false positive rate (F...