In our laboratory, we aim to understand the dynamics of diseases and immunity at the molecular level to overcome diseases such as cancer through the analysis of vast amounts of biomolecular data from many clinical specimens using mathematical/computational techniques like deep learning and quantum computing. It is expected that medical care will shift towards patient specific optimal therapies and drug dosages to treat and prevent disease. To do so, we need to develop new mathematical scientific methodologies based and analyze big data in human biomedical sciences, such as spatio-temporal omics molecular data, imaging data, and clinical information. In this way, we will discover new causes of disease and reconstruct quantitative networks of the relationships among these contributing factors to understand disease mechanisms as a whole system. As an example, by unraveling and quantitatively reconstructing the relationships of cells in the tumor microenvironment, including the immune system, we can predict the dynamics of response to treatment, side effects, and acquisition of resistance for each patient.