Professor
Graduate School of Science and Technology
Nara Institute of Science and Technology
Yoshimasa Kawazoe | Project Associate Professor Graduate School of Medicine The University of Tokyo |
Satoko Hori | Professor Faculty of Pharmacy Keio University |
In the age of DX, an enormous amount of medical data is being collected in the form of natural languages, such as technical documents, electronic medical records, and pharmacy reports. We are developing a platform for natural language processing of this vast amount of text data. Our goal is to automatically and massively discover optimal drugs and combinations of drugs that reduce adverse events associated with cancer treatment.
Associate Professor
The Institute for Solid State Physics
The University of Tokyo
Ichiro Takeuchi | Professor Graduate School of Engineering Nagoya University |
Kazuhito Tabata | Associate Professor Graduate School of Engineering The University of Tokyo |
In this study, two experimental systems, an automated functional evaluation system and a digital bioassay method, will be constructed to obtain large-scale data on amino acid sequences and functionalities of proteins. In addition, we will link these experimental data to a machine learning system online in real time to realize an automated research loop that automatically plans the next experiment. This will enable the realization of next-generation innovative optogenetics tools.
Team Leader
Center for Integrative Medical Sciences
RIKEN
Misako Takayasu | Professor School of Computing Tokyo Institute of Technology |
Lena Takayasu | visiting fellow Graduate School of Medicine The University of Tokyo |
Despite the plethora of evidence showing the powerful effects of the gut microbiota in human disease, little progress has been made towards creating revolutionary new therapeutics aimed at preventing or intervening in disease progression. We believe this lack of progress can be attributed to our fragmented understanding of the complex biology underlying host-microbiome crosstalks. In this study, we will aim to elucidate host-microbiome crosstalks by scrutinizing the changes in microbiome compositions over time at high resolution. We will achieve this by automating the process of sample collection. Our goal is to identify universal properties underlying gut microbiome prediction and control.
Professor
Research Institute for Electronic Science
Hokkaido University
Yuichi Iino | Professor Graduate School of Science The University of Tokyo |
Masato Taki | Associate Professor Graduate School of Artificial Intelligence and Science Rikkyo University |
Teppei Matsui | Professor Graduate School of Brain Science Doshisya University |
We aim to contribute to the next-generation Bio-DX by establishing high-speed, high-dimensional closed-loop optical measurement technology, a novel experimental method that breaks through the limitations of conventional large-scale measurement and analysis. We will develop technologies based on advanced photonics and AI technologies, and apply the developed devices to studies of the nervous system of mice and nematodes to elucidate its mechanisms, which have been difficult to analyze in a realistic amount of time using conventional experimental approaches.
Team Leader
Center for Integrative Medical Sciences
RIKEN
Yoshihiro Izumi | Associate Professor Medical Institute of Bioregulation Kyushu University |
Takahiro Kato | Associate Professor Graduate School of Medical Sciences Kyushu University |
Genshiro Sunagawa | Team Leader Center for Biosystems Dynamics Research RIKEN |
Tsukasa Fukunaga | Associate Professor Institute for Advanced Study Waseda University |
Seasonal affective disorder and hibernation/torpor-like behaviors have several similar phenotypes. We examine whether or not there are shared metabolic regulatory networks that underlie these two phenomena. We record video clips of mouse behavior and automatically identify their phenotypes using motion analysis AI. Biological samples taken from mice and human patients will be subjected to automated absolute quantitative omics measurements and subsequent trans-omics analysis to reconstruct metabolic regulatory networks of seasonal affective disorder and hibernation/torpor-like behaviors. Divergence dating of molecules that belong to the reconstructed networks will be performed to examine if seasonal affective disorder and hibernation share an evolutionary origin.