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

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.<br />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". <br />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.

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

The 143rd Annual Meeting of the Pharmaceutical Society of Japan (Sapporo)

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

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