[S14] Current status and challenges of research for genomic medicine and food safety using large-scale data

Sun. Mar 26, 2023 1:50 PM - 3:50 PM [Room D] Institute for the Advancement of Higher Education: S2 (Bldg. S: 2F)

Organizer: Kazunari Kondo, Yayoi Natsume-Kitatani

The use of statistical analysis and AI technology is indispensable for research from omics analysis to genome drug discovery and personalized medicine. Advanced research is also ongoing to discover disease-related genes and elucidate pathological conditions through integrated analysis of genome cohort information using Biobanks, and to link this to drug discovery. At the same time, the number of situations in which data of different natures, such as omics data and medical data, are handled simultaneously is increasing, and the establishment of analysis methods is also progressing. In addition, in silico prediction has become a very important research field in the field of risk assessment.
In the first half of the symposium, leading researchers in these research fields will talk about "Statistical genetics, disease biology, drug discovery, and personalized medicine" and "Data-driven drug target discovery by subset binding, a stratification algorithm that links heterogeneous data". The second half will focus on "Development of a new in silico allergen prediction method using machine learning and deep learning" and "Use of AI technology and its applicability to risk assessment in the medical and other fields".
In this symposium, we would like to share the current status, challenges, and approaches of the latest research on genomic drug discovery and personalized medicine, and at the same time, we would like to help you develop your research by further advancing your understanding of how AI is used and the challenges it faces in the field of risk assessment. We hope that this symposium will help you to develop your research.

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オーガナイザー挨拶：近藤一成（国立医薬品食品衛生研） (1:50 PM - 1:55 PM)

1:55 PM - 2:20 PM

[S14-01] Statistical genetics, disease biology, drug discovery, and personalized medicine

○Yukinori Okada^1,2,3 (1. Dep. Stat Gen, Grad Sch Med, Univ Tokyo, 2. Dep. Genom Info, Osaka Univ Grad Sch Med, 3. RIKEN IMS)

2:20 PM - 2:45 PM

[S14-02] Data-driven drug target discovery by “subset binding”, a stratification algorithm that links heterogeneous data

○Yayoi Natsume-Kitatani^1,2 (1. NIBIOHN, 2. Instit. Advanced Med. Sci., Tokushima Univ.)

2:45 PM - 3:10 PM

[S14-03] Development of a new allergenicity prediction method using machine learning and deep learning

○Norimasa Tamehiro^1,2 (1. FSC, 2. Natl. Inst. Health Sci.)

3:10 PM - 3:35 PM

[S14-04] Use of AI technology and its applicability to risk assessment in the medical and other fields

○Reiko Adachi¹ (1. Natl. Inst.Heath Sci.)

討論・総括：夏目やよい（医薬健栄研） (3:35 PM - 3:50 PM)