iPRISM: Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling

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.

Version:	0.1.1
Depends:	R (≥ 4.1.0)
Imports:	ggplot2, Hmisc, tidyr, igraph, pbapply, Matrix, methods
Suggests:	knitr, rmarkdown
Published:	2024-07-14
DOI:	10.32614/CRAN.package.iPRISM
Author:	Junwei Han [aut, cre, ctb], Yinchun Su [aut], Siyuan Li [aut]
Maintainer:	Junwei Han <hanjunwei1981 at 163.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
CRAN checks:	iPRISM results

Documentation:

Reference manual:	iPRISM.pdf
Vignettes:	iPRISM User Guide

Downloads:

Package source:	iPRISM_0.1.1.tar.gz
Windows binaries:	r-devel: iPRISM_0.1.1.zip, r-release: iPRISM_0.1.1.zip, r-oldrel: iPRISM_0.1.1.zip
macOS binaries:	r-release (arm64): iPRISM_0.1.1.tgz, r-oldrel (arm64): iPRISM_0.1.1.tgz, r-release (x86_64): iPRISM_0.1.1.tgz, r-oldrel (x86_64): iPRISM_0.1.1.tgz

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