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 |
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