5 papers
PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction
Yoshitaka Inoue, Minoh Jeong, Alfred Hero +2
Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug rep…
User-Guided Force-Directed Graph Layout
Hasan Balci, Augustin Luna
Visual analysis of relational data is essential for many real-world analytics tasks, with layout quality being key to interpretability. However, existing layout algorithms often re…
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…
DrugAgent: Reliable Multi-Agent Integration of Conflicting Biomedical Evidence for Drug-Target Interaction Assessment
Yoshitaka Inoue, Tianci Song, Xinling Wang +3
Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observations from experimental literatur…
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…