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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 |
| Reference manual: | iPRISM.pdf |
| Vignettes: |
iPRISM User Guide |
| 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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These binaries (installable software) and packages are in development.
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