5 citations · 7 across the 6 of their papers we have counts for
3 papers · 1 filter
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…
Protein-Mamba: Biological Mamba Models for Protein Function Prediction
Bohao Xu, Yingzhou Lu, Yoshitaka Inoue +3
Protein function prediction is a pivotal task in drug discovery, significantly impacting the development of effective and safe therapeutics. Traditional machine learning models oft…
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…