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iPRISM (version 0.1.1)

Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling

Description

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.

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Version

Install

install.packages('iPRISM')

Monthly Downloads

135

Version

0.1.1

License

GPL (>= 2)

Maintainer

Junwei Han

Last Published

July 14th, 2024

Functions in iPRISM (0.1.1)

get_logiModel

Fit Logistic Regression Model
pred_value

Original Class Labels for Samples
Seeds

Seed Node Names
ESscore_weighted

Weighted Enrichment Score Calculation
ESscore

Enrichment Score Calculation
data_sig

data_sig
path_list

path_list
genelist_cp

TME gene list after random walks
genelist_imm

ICI gene list after random walks
ppi

A protein-protein physical interaction network (PPI network)
iPRISM-package

Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling
gseafun

Gene Set Enrichment Analysis (GSEA) Function
get_gsea_path

Gene Set Enrichment Analysis (GSEA) using Multiplex Networks
genelist_hla

HLA gene list after random walks
data.path

data.path
data.cell

data.cell
cor_plot

Correlation Plot with Significance Points