ESscore_weighted

Calculates the weighted enrichment score (ES) for a given set of labels and correlation vector.

Immunotherapy has revolutionized cancer treatment, but predicting patient
response remains challenging. Here, we presented Intelligent Predicting
Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a
novel network-based model that integrates multiple data types to predict
immunotherapy outcomes. It incorporates gene expression, biological
functional network, tumor microenvironment characteristics, immune-related
pathways, and clinical data to provide a comprehensive view of factors
influencing immunotherapy efficacy. By identifying key genetic and
immunological factors, it provides an insight for more personalized
treatment strategies and combination therapies to overcome resistance
mechanisms.

Junwei Han

iPRISM

Intelligent Predicting Response to Cancer Immunotherapy Through
Systematic Modeling

Yinchun Su

Siyuan Li

ESscore_weighted function

<dl><dt>labels.list</dt>
<dd>A binary vector indicating membership in a gene set (1 for inclusion, 0 for exclusion).</dd>
<dt>correl.vector</dt>
<dd>A vector of correlation values (e.g., gene expression correlations).</dd></dl>

Arguments

Weighted Enrichment Score Calculation — ESscore_weighted

<dl>

<dt>labels.list</dt>
<dd>A binary vector indicating membership in a gene set (1 for inclusion, 0 for exclusion).</dd>


<dt>correl.vector</dt>
<dd>A vector of correlation values (e.g., gene expression correlations).</dd>

</dl>

ESscore_weighted: Weighted Enrichment Score Calculation

Description

Usage

Value

Arguments