Nowadays, biomedical big data of clinical and omic profiles are collected from hospitals and medical institutions. First, applying data-mining methodologies, we explore etiologies of intractable diseases, e.g. cancer, common diseases, and neurodegenerative diseases. Next, we classify each disease into finer categories through molecular profiles, and understand disease causing mechanisms through a systems approach. In this way, we can collect knowledge of disease incidence and progression based on clinical and omic data. Last, we apply mathematical methods, e.g, machine learning techniques, to optimize therapy prediction for each patient when she/he visits a hospital/medical institute, and we can also apply these methods to disease prevention based on an individual’s health check records. Our department does biomedical research and genomic medicine research driven by prediction through complete utilization of advanced mathematics and computational sciences with the goal of developing personalized/precision/preventive medicine strategies.

■Exploration of disease etiologies driven by integrative analysis of clinical and omic data
· Search for somatic mutations that cause hepatocellular carcinoma
· Search for causes of long QT syndrome, congenital neurologic disorder, etc.

■Molecular classification of and a systems approach to understanding disease based on omic profiling
・Subtype classification of liver cancer with multi-omic profiling

■Prediction for precision medicine

■Development of methodologies for the above