Journals

1. 2026
   DrugAgent: Reliable Multi-Agent Aggregation under Conflicting Biomedical Evidence

   Inoue, Y., Song, T., Wang, X., Luna, A., & Fu, T.

   Scientific Reports (under revision).

   CodeRelated OpenReview paper
2. 2026
   Drug Discovery in the Era of Artificial Intelligence: From Target Identification to Clinical Trials

   Inoue, Y., Hao, N., Lu, Y., Fu, T., & Luna, A.

   Health Data Science (accepted).

3. 2026
   TRPM4 Expression as a Predictive Biomarker and a Mechanistic Driver of Acetalax Activity in Prostate Cancer: Preclinical Efficacy Studies

   Hoshi-Kadonosawa, Y., Reinhold, W. C., Taniyama, D., Inoue, Y., et al.

   Molecular Cancer Therapeutics.

   DOIPubMed
4. 2026
   drGT: Interpretable Drug Response Prediction with Attention-Guided Gene Attribution on a Drug-Cell-Gene Heterogeneous Graph

   Inoue, Y., Lee, H., Fu, T., Kuang, R., & Luna, A.

   BMC Bioinformatics.

   DOICodePubMed
5. 2026
   CellMiner cross-database (CellMinerCDB) version 2.2 for explorations of patient-derived cancer cell line pharmacogenomics

   Elloumi, F., Reinhold, W. C., Varma, S., et al., Inoue, Y., et al.

   Nucleic Acids Research.

   DOIPubMed

Conferences & preprints

1. 2026
   Dynamic Schlafen 11 (SLFN11) expression revealed by single-cell RNA sequencing of clonal human cancer cells

   Taniyama, D., Inoue, Y., Elloumi, F., Kim, Y. S., Roper, N., Seibert, C., Kelly, M., Lobanov, A., Aladjem, M. I., Luna, A., & Pommier, Y.

   bioRxiv.

   Preprint
2. 2026
   DrugAgent: Reliable Multi-Agent Aggregation under Conflicting Biomedical Evidence

   ICML 2026 Workshop on FM4LS.

   Code
3. 2025
   GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction

   ICLR 2025 Workshop on MLGenX · arXiv:2504.05454.

   arXivCodePubMed
4. 2025
   DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning

   ICLR 2025 Workshop on MLGenX.

   arXivCode
5. 2024
   BiGCN: Leveraging Cell and Gene Similarities for Single-cell Transcriptome Imputation with Bi-Graph Convolutional Networks

   Inoue, Y., Kulman, E., & Kuang, R.

   bioRxiv.

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