5 citations · 7 across the 6 of their papers we have counts for
4 papers · 1 filter
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…