Fig. 1: Genomic landscape of machine learning in tumor immunotherapy.
From: Informing immunotherapy with multi-omics driven machine learning
We provide an overview of machine learning methodologies applicable to different aspects of tumor immunotherapy including prediction of immunotherapy responses, identification of response-associated biomarkers, and analysis of the TME. ML machine learning, DEG differentially expressed gene, RFE recursive feature elimination, UAF univariate association filtering, LASSO the least absolute shrinkage and selection operator, LR logistic regression, SVM support vector machine, RF random forest, FCNN fully-connected neural network, CNN convolutional neural network, RNN recurrent neural network, PFS progression-free survival, TME tumor microenvironment.
