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cs.LG2025
GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction
Yoshitaka Inoue, Tianfan Fu, Augustin Luna
Explainability is necessary for many tasks in biomedical research. Recent explainability methods have focused on attention, gradient, and Shapley value. These do not handle data wi…
cs.LG2024
drGT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network
Yoshitaka Inoue, Hunmin Lee, Tianfan Fu +2
For translational impact, both accurate drug response prediction and biological plausibility of predictive features are needed. We present drGT, a heterogeneous graph deep learning…