HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients



Summary

HAPIR is a machine learning method designed to predict a cancer patient’s response to immunotherapy. It leverages Hallmark gene sets, which represent well-defined biological processes, to train its prediction model. This approach focuses on identifying key gene expression patterns within these Hallmark sets that are indicative of immunotherapy success or failure. By using refined Hallmark gene set data, HAPIR aims to provide a more accurate and robust prediction of immunotherapy response, potentially leading to better treatment decisions and improved patient outcomes.

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