Top

Understand Life Phenomena and Diseases with Advanced Mathematical Science and Molecular Observation

The goal of our research is to overcome diseases such as cancer by analyzing large-scale data on biomolecules from numerous clinical samples using deep learning and quantum computing, thereby elucidating the role of biological phenomena—such as the immune response—in these diseases. This is expected to enable the delivery of appropriate types and doses of treatment personalized for each patient, as well as the prevention of disease onset. To achieve this, we must devise new mathematical and scientific methodologies based on deep learning and quantum computing, and analyze big data in human life sciences—including spatiotemporal omics molecular data, image data, and clinical information. By discovering new causes of disease in this way and reconstructing the relationships between these factors as quantitative networks, we can understand disease mechanisms as holistic systems. As one example, by unraveling and quantitatively reconstructing the relationships between cancer cells and the tumor microenvironment—including the immune system—and incorporating their underlying structures, we can predict the dynamics of treatment efficacy, side effects, and the development of resistance on an individual basis.

Advancing cutting-edge medical science by elucidating diseases such as cancer through new mathematical sciences!

Research Topics

• Development of predictive models for treatment response with deep learning and quantum computing
• Elucidating relationships among omics molecules with deep learning and quantum Computing
• Elucidating interactions among cancer, immune, and stromal cells within the tumor microenvironment
• Development of mathematical simulation models for biomedical phenomena
• Development of methods for identifying biomolecules via signal analysis of nanopore sensors