Breast cancer research that makes use of data science is currently progressing

For many years Professor Kazunori Akimoto has been analyzing stem cells, the major source of cancer, from the perspective of pharmaceutical research. In the course of this analysis, he began to think that it might be possible to utilize data science in cancer research. “There are a large number of data science experts at Tokyo University of Science,” he explains. “This makes it the perfect environment for applying the results of this kind of research to medical care.” Cancer gene panel testing has been approved for conditional coverage under the Japanese National Health Insurance system. As a result, the issue of how cancer patients’ genetic information is used is a problem that will have to be addressed in the future. At Tokyo University of Science, efforts are currently underway to extract bio markers from genes that will predict late recurrence 10 to 15 years after a diagnosis of breast cancer. “If recurrence 10 to 15 years in the future when the expression of a certain gene is high can be anticipated at the time the diagnosis is made,” says Professor Akimoto, “it would then be possible to make the necessary preparations to reduce this risk. The use of information theory methods in research that we conducted in conjunction with data science experts has allowed us to find the gene that is the biomarker for this.”

The application of artificial intelligence technologies that have been developed in a variety of fields to the field of medical research

Professor Hiroyuki Nishiyama has been involved for many years in applied research concerning artificial intelligence and communication using computer networks. Examples of his research include his study of a system that monitors drivers for drowsiness while behind the wheel and his study of dairy farming using milking and suckling robots. Specifically, he is developing AI systems that can safely and accurately perform tasks in place of humans. He has utilized high-speed logic machine learning particularly in his dairy research. “If we are unable to explain the reasons why certain cows have been selected as ‘good cows’ based on the data we have gathered, then no one would be convinced that our system is working,” says Professor Nishiyama. “The use of logic-based machine learning, however, allows us to explain that cows that wake up early and eat at a certain speed at each meal are the healthy ones and therefore ‘good’ cows.” This research is expected to be useful in the analysis of cancer gene expression data as well; that is, it is the kind of research that will provide solid advice to medical practitioners. 

The research environment at Tokyo University of Science combines copious knowledge from both data science and medical treatment

Researchers in the Data Science and Medical Research Division are not only from the Faculty of Science and Technology and the Faculty of Pharmaceutical Sciences. In addition, there are many researchers from the Faculty of Engineering, the Faculty of Science Divisions I and II, and the Research Institute for Biomedical Sciences. Preventing cancer and extending healthy lifespans are the goals of the development and implementation of an infrastructure for cancer genomics data science and medical treatment. At the same time, through organizing an interdisciplinary research field by researchers of TUS and the National Cancer Center, they aim to accelerate research into the treatment, diagnosis, and prevention of cancer. Gene therapy that utilizes therapeutic methods based on genetic mutations in cancer patients has only just begun in Japan. Cancer multiomics data science-based treatment is a major trend in both cancer and medical research throughout the world, and it would not be an exaggeration to say that Tokyo University of Science is playing a role in areas that are important to the development of this field.