5 citations · 7 across the 6 of their papers we have counts for
6 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…
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…
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…
scVGAE: A ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation
Yoshitaka Inoue
Single-cell RNA sequencing (scRNA-seq) provides high-resolution measurements of cellular heterogeneity, but sparsity and technical zeros can obscure biological structure and compli…