KEGG_cancer_pathways_descriptions

A data frame containing descriptions for KEGG 'Pathways in cancer'
(hsa05200)-related pathways.
Generated on Nov 17, 2020.

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>.

Ege Ulgen

driveR

Prioritizing Cancer Driver Genes Using Genomics Data

KEGG_cancer_pathways_descriptions function

A data frame with 21 rows and 2 variables:<dl>
<dt>id</dt>
<dd>KEGG pathway ID</dd><dt>description</dt>
<dd>KEGG pathway description</dd>
</dl>

Format

KEGG 'Pathways in cancer'-related Pathways - Descriptions — KEGG_cancer_pathways_descriptions

A data frame with 21 rows and 2 variables:<dl>
<dt>id</dt>
<dd>KEGG pathway ID</dd>

<dt>description</dt>
<dd>KEGG pathway description</dd>


</dl>

KEGG_cancer_pathways_descriptions: KEGG 'Pathways in cancer'-related Pathways - Descriptions

Description

Usage

Arguments

Format