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ModelsChowell2022 - Random Forest model to predict efficacy of immune checkpoint blockade across multiple cancer patient cohorts
This is a Random Forest algorithm-based machine learning model called RF16, which incorporates a total of 16 genomic, molecular, demographic, and clinical features to predict the immunotherapy response for a patient. The model assigns a value of 0 for NonResponder and 1 for Responder. Please be awar...
2023-05-09 | BIOMD0000001066 | BioModels
Machine Learning Model FAIR-AIML 
ModelsDiego2021 - Improved Prediction of ICB Efficacy Across Multiple Cancer Types using Random Forests
In this study, the authors had developed a machine learning model to predict immune checkpoint blockade (ICB) response by integrating genomic, molecular, demographic and clinical data from a curated cohort (MSK-IMPACT) with 1479 patients treated with ICB across 16 different types of cancer. This mod...
2024-07-23 | MODEL2407210002 | BioModels
Machine Learning Model 
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