Toshiya Iwamori

Publications

• Improving Transcriptomic Foundation Models Through Gene-Level Error Analysis and Adaptive Masking

  • • Michael Morris Danziger
    • Bharath Dandala
    • et al.
  • 2025
  • ISMB 2025

• Improving Health-Status Classification for Epithelial Cells of Inflammatory Bowel Disease using a Transcriptomic Biomedical Foundation Model

  • • Toshiya Iwamori
    • Vibha Anand
    • et al.
  • 2024
  • ISMB 2024

• DPM360: New Additions to Advanced Disease Progression Modeling

  • • Akira Koseki
    • Italo Buleje
    • et al.
  • 2022
  • OHDSI Collaborator Showcase 2022

• Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Prediction

  • • Takayuki Katsuki
    • Kohei Miyaguchi
    • et al.
  • 2022
  • IJCAI 2022

• Development of a machine learning-based prediction model for extremely rapid decline in estimated glomerular filtration rate in patients with chronic kidney disease: a retrospective cohort study using a large data set from a hospital in Japan

  • • Daijo Inaguma
    • Hiroki Hayashi
    • et al.
  • 2022
  • BMJ Open

• Variational Inference for Discriminative Learning with Generative Modeling of Feature Incompletion

  • • Kohei Miyaguchi
    • Takayuki Katsuki
    • et al.
  • 2022
  • ICLR 2022

• Prescription rate of erythropoietin-stimulating agents is low for patients with renal impairment under non-nephrology care in a tertiary-level academic medical center in Japan

  • • Naoki Okamoto
    • Daijo Inaguma
    • et al.
  • 2022
  • Clinical and Experimental Nephrology

• A Comparative Time-to-Event Analysis Across Health Systems

  • • Mohamed Ghalwash
    • Prithwish Chakraborty
    • et al.
  • 2021
  • AMIA Annual Symposium 2021

• Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data

  • • Subhrajit Roy
    • Isabell Kiral
    • et al.
  • 2021
  • EBioMedicine

• Increasing tendency of urine protein is a risk factor for rapid EGFR decline in patients with CKD: A machine learning-based prediction model by using a big database

  • • Daijo Inaguma
    • Akimitsu Kitagawa
    • et al.
  • 2020
  • PLoS ONE