metapredictor_model

<p>A Random Forest model object for metaprediction of coding variants' impact,
using 12 impact scores from different coding impact predictors. The model was
trained on 711 coding variants, with 10-folds repeated 3 times cross-validation.</p>

datasets

Cancer genomes contain large numbers of somatic alterations but few
genes drive tumor development. Identifying cancer driver genes is critical
for precision oncology. Most of current approaches either identify driver
genes based on mutational recurrence or using estimated scores predicting
the functional consequences of mutations. 'driveR' is a tool for
personalized or batch analysis of genomic data for driver gene prioritization
by combining genomic information and prior biological knowledge. As features,
'driveR' uses coding impact metaprediction scores, non-coding impact scores,
somatic copy number alteration scores, hotspot gene/double-hit gene
condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG
pathways. It uses these features to estimate cancer-type-specific
probability for each gene of being a cancer driver using the related task of
a multi-task learning classification model. The method is described in detail
in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for
prioritizing cancer driver genes using somatic genomics data. BMC
Bioinformatics <doi:10.1186/s12859-021-04203-7>.

metapredictor_model: Random Forest Model for Coding Impact Metaprediction

Description

Usage

Arguments

Format