基于机器学习的肿瘤免疫治疗应答预测研究

肿瘤免疫治疗应答的预测对肿瘤治疗方案设计及治疗有着重要的意义。本文引入基于随机森林的机器学习方法,将病人黑色素瘤组织转录组RNA-seq的基因表达谱作为特征,对免疫检查点阻断治疗的结果进行预测研究。对病人的基因表达谱使用随机森林算法来构建预测模型,并与Logistic回归模型和XGBoost模型进行比较。实验结果表明,随机森林模型对免疫检查点阻断治疗的应答能够进行较准确的预测,并且较Logistic回归模型和XGBoost模型预测效果更好。

Research of Prediction of the Response to Tumor Immunotherapy Based on Machine Learning

Prediction of the response to tumor immunotherapy is of great significance to the design of tumor treatment and treatment. In this paper, random forest machine learning method is introduced, and gene expression profile of patients'melanoma RNA-seq was taken as characteristics to predict the response to immune checkpoint blockade.Random forest algorithm was used to construct the prediction model for the gene expression profile of patients , and compared with Logistic regression analysis and XGBoost algorithm. The experimental results show that random forest model had a great prediction accuracy to the response to immune checkpoint blockade and was better than Logistic regression model and XGBoost model.