[Bio-DX]Innovation of Life Science through Digital Transformation Focused on Data-Driven and AI-Driven Technologies

Strategic Objective

Toward scientific discoveries through DX in life science research

Research supervisor

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Yasushi Okada(Team Leader, Center for Biosystems Dynamics Research, RIKEN)

Assistant Supervisor

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Koichi Takahashi(Team Leader, Center for Biosystems Dynamics Research, RIKEN)

Overview

 Introducing information technology with the aim of comprehensive and innovative progress that focuses on venturing into the state of operational processes, the way organizations work, and even the culture of each field, rather than simply making operations more effective, is known as digital transformation (DX). Scientific research, especially that of the life science fields, is one of the areas DX is focusing on for innovation. In fact, since the introduction of deep learning, there has been remarkable integration between the biology field and AI research, and research in image analysis, predicted risks for disease, searches for new medicines, and estimations of protein 3D structures are being accelerated around the world.
 One of the reasons that DX is particularly anticipated in life sciences is because life systems range from a single protein molecule to ecosystems; characteristically, the focus of life science research is large-scale, complex systems in which thousands of types of components or more interact with each other. While the progress of computational technologies and the automation of measuring and experiments means that we can obtain huge amounts of digital data from these complex systems at high speed, we have already reached a position in which it is no longer possible to analyze everything with the thinking capabilities of humans.
 Therefore, this research area aims to bring together the “trinity” of information science, engineering, and life science, and to make advanced scientific discoveries that were not previously within our reach using “data-driven and AI-driven” research that promotes DX in life science research.
 More specifically, we aim to (1) Carry out research and development that will overcome the qualitative and quantitative limits of data acquisition and analysis from varied and large data sets by making use of digital processing technologies such as AI, making new life phenomena and their models possible, and providing model cases for next generation life science research. Furthermore, we will (2) Support research and development that will establish innovative techniques for data-driven research and techniques for AI-driven research, which are necessary elemental challenges to accomplish this.
 By doing the above, we aim to realize a society in which we can create a tide leading to a paradigm shift in life science research, as well as clarify complex life systems; a society in which researchers can focus on truly creative activities.
 This research area will be operated as part of the Ministry of Education, Culture, Sports, Science and Technology (MEXT)’s Artificial Intelligence/Big Data/IoT (Internet of Things)/Cybersecurity Integration Project (AIP Project : Advanced Integrated Intelligence Platform Project).

Research Area Advisors

Sadao Ota Associate Professor, Research Center for Advanced Science and Technology, The University of Tokyo
Hisashi Kashima Professor, Graduate School of Informatics, Kyoto University
Masayo Takahashi President, Vision Care Inc.
Haruko Takeyama Professor, Faculty of Science and Engineering, Waseda University
Tohru Natsume Prime Senior Researcher, Cellular and Molecular Biotechnology Research Institute, The National Institute of Advanced Industrial Science and Technology
Nozomu Yachie Associate Professor, School of Biomedical Engineering, The University of British Columbia
Ryo Yoshida Professor, The Institute of Statistical Mathematics, Inter-University Research Institute Corporation Research Organization of Information and Systems

Quick Access

Program

  • CREST
  • PRESTO
  • ACT-I
  • ERATO
  • ACT-X
  • ACCEL
  • ALCA
  • RISTEX
  • manual
  • AIP Network Lab
  • Global Activities
  • Diversity
  • SDGs
  • OSpolicy
  • Yuugu
  • Questions