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Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study

Wang et al [22] introduced a deep learning framework, Deep Learning for Drug-Drug Synergy prediction (Deep DDS), for predicting drug-drug interactions for anticancer treatments. Deep DDS uses gene expression data from the cancer cell line and the molecular graph of the drugs as input. It leverages GAT and graph convolution transformers (GCTs) to accurately predict the synergistic effect between drug combinations. Deep DDS has achieved an AUROC score of 0.67 on an independent test set.

Xinyue Hu, Zenan Sun, Yi Nian, Yichen Wang, Yifang Dang, Fang Li, Jingna Feng, Evan Yu, Cui Tao