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Artificial Intelligence (AI) Medicine Lab
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A lab that reads big data in medicine with machine learning and mathematics, and applies it to prediction and personalized medicine.
AboutAbout the lab
Reading life phenomena in the language of mathematics.
The lab works on building a mathematical foundation for analyzing big data in medicine and biology.
It aims to understand life phenomena from the large amounts of data made available by advances in measurement technology.
Using machine learning and applied mathematics, the lab develops algorithms for patient stratification and disease prediction.
The lab also teaches courses at the School of Medicine, such as Introduction to AI Mathematical Sciences.
Key terms
- Machine learning
- A computational approach that automatically finds rules in large amounts of data and uses them for classification and prediction
- Stratification
- Dividing patients with the same diagnosis into groups according to their physical condition or data characteristics
- Pathway
- The route within a cell along which molecules act on one another in turn to pass on signals or reactions
NewsLab news
Project Assistant Professor Fuse receives the NEURO2026 Rising Neuroscientist Award (NEURO2026 (the 49th Annual Meeting of the Japan Neuroscience Society, the 69th Annual Meeting of the Japan…)
Paper by Professor Kawakami et al. published in Ophthalmology Science
Paper by Professor Kawakami et al. published in Medical Science Educator
Assistant Professor Takahisa Kawano joins the lab on a cross-appointment
Paper by Project Associate Professor Gu et al. published in APOPTOSIS
ResearchResearch themes
The lab's goal is to establish a mathematical foundation for understanding life phenomena and applying that understanding to prediction and personalized medicine. Its research rests on three pillars: network-based prediction of regulatory factors, stratification by machine learning, and state transition models and future prediction.
*Illustrative imagePredicting Regulatory Factors with Networks
The lab aims to incorporate information on protein localization and function into protein-protein interaction networks, and to use machine learning to build pathway models grounded in intracellular processes. It will also develop algorithms that estimate the influence of upstream factors from gene expression data.
*Illustrative imageStratifying Patients with Machine Learning
The lab develops machine learning algorithms that can handle medical and biological data with many missing values and much noise, and applies them to clinical data for patient stratification and biomarker discovery. It also reanalyzes RNA-seq data from public databases and works on mapping correspondences between humans and mice.
*Illustrative imageState Transition Models and Future Prediction
The lab views diseases and life phenomena as rule-governed transitions between states. It applies machine learning and applied mathematics to time-series data measured at irregular times, and develops methods that predict the future using state-space models and energy landscape analysis.
MembersFaculty
The lab's members consist of faculty, project researchers, visiting faculty, and doctoral students.As of 2026
- ProfessorEiryo Kawakami
- Associate ProfessorTazro Ohta
- Project Associate ProfessorWenchao Gu
- Project LecturerMasako Yamazaki
- Project Assistant ProfessorAkiko Takahashi
- Project Assistant ProfessorNatsuko Otaki
- Project Assistant ProfessorYutaro Fuse
- Project Assistant ProfessorKatsuyuki Chida
- Assistant ProfessorTakahisa Kawano
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Human interactome” Keiono / CC BY-SA 3.0 (cropped)
- Predicting Regulatory Factors with Networks: “Multiplex Human HIV-1 protein-protein interaction network (edge-colored visualization)” Dedalus1234 / CC BY-SA 4.0 (cropped)
- Stratifying Patients with Machine Learning: “Patient lying in hospital bed in intensive care unit in Germany in 2015” Pittigrilli / CC BY-SA 4.0 (cropped)
- State Transition Models and Future Prediction: “Free-Energy-Landscape-Closeup” Tyler Savitski / CC BY-SA 4.0 (cropped)
- Related lab (Emergency and Critical Care Medicine Lab): “20250808 Intensive care unit bed” Ismael Olea / CC BY 4.0 (cropped)
- Related lab (Innovative Regenerative Medicine Lab): “Hematopoietic Stem Cell (NIH BioArt 200)” Courtesy of NIAID Ryan Kissinger / Public domain (cropped)
- Related lab (Endocrinology, Metabolism, and Geriatric Medicine Lab): “Diabetes health cheack” Ofek Abuhazira / CC BY-SA 4.0 (cropped)
- Related lab (Advanced Data Medical Science Lab): “Illumina Hiseq 2000 sequencers, BGI Hong Kong sequencing room” Scotted400 / CC BY 3.0 (cropped)
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