example_driveR_res

Data frame containing 'driveR' results for a lung adenocarcinoma case.

datasets

Identification of the most appropriate pharmacotherapy for each
patient based on genomic alterations is a major challenge in personalized
oncology. 'PANACEA' is a collection of personalized anti-cancer drug
prioritization approaches utilizing network methods. The methods utilize
personalized "driverness" scores from 'driveR' to rank drugs, mapping these
onto a protein-protein interaction network. The "distance-based" method
scores each drug based on these scores and distances between drugs and genes
to rank given drugs. The "RWR" method propagates these scores via a
random-walk with restart framework to rank the drugs. The methods are
described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA:
network-based methods for pharmacotherapy prioritization in personalized
oncology. Bioinformatics <doi:10.1093/bioinformatics/btad022>.

Ege Ulgen

PANACEA

Personalized Network-Based Anti-Cancer Therapy Evaluation

example_driveR_res function

a data frame containing 106 rows and 3 variables:<dl>
<dt>gene_symbol</dt>
<dd>HGNC gene symbol</dd><dt>driverness_prob</dt>
<dd>'driverness' probability</dd><dt>prediction</dt>
<dd>driveR's prediction for whether the gene is a 'driver' or 'non-driver'</dd>
</dl>

Format

Example driveR Result — example_driveR_res

a data frame containing 106 rows and 3 variables:<dl>
<dt>gene_symbol</dt>
<dd>HGNC gene symbol</dd>

<dt>driverness_prob</dt>
<dd>'driverness' probability</dd>

<dt>prediction</dt>
<dd>driveR's prediction for whether the gene is a 'driver' or 'non-driver'</dd>


</dl>

example_driveR_res: Example driveR Result

Description

Usage

Arguments

Format