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CE Credit Available

About the Presentation:

Artificial Intelligence (AI) is a broad concept of training machines to think and behave like humans. It consists of a wide range of statistical and machine learning approaches to learn from the existing data/information to predict future outcomes. It has impacted a board range of scientific disciplines that are important to public health, ranging from clinical diagnosis and prognosis, drug and food safety, disease prevention, precision medicine and nutrition. The rise of AI has also offered both opportunities and challenges to regulatory agencies with questions such as (1) how to assess and evaluate AI-based products and (2) how to develop and implement AI-based application to improve the agencies functions. In this presentation, the current thinking and on-going efforts at NCTR in the area of AI will be discussed with examples from drug and food safety, natural language processing of regulatory documents, and biomarker discovery and development. The guiding principle and best practice of applying AI in regulatory science research will also be discussed with respect to the context of use and fit-for-purpose application.

What you’ll learn from this FDA Animal Scientist:

• Explain the basic principles and methodologies of AI
• Describe different AI methods
• Describe ways in which AI methods can be applied for drug and food safety, biomarker development and text mining

Presented by:

Weida Tong, PhD
Division Director, NCTR
National Center for Toxicological Research (NCTR), FDA

About the Speaker:

Dr. Tong is the Director of Division of Bioinformatics and Biostatistics at NCTR/FDA. He has served on the Science Advisory Board for several multi-institutional projects in Europe and USA. He also holds adjunct appointments at several universities. He is the founder and board chairperson of international MAQC Society, President of MCBIOS Society, and Chair of SOT Board of Publication. His primary research interest is to apply bioinformatics, Artificial Intelligence, molecular modeling and data analytics for biomarker discovery, drug safety and repurposing, pharmacogenomics/toxicogenomics, and precision medicine. Currently, he directs several FDA mission-critical projects in his division: (1) Supervising and leading the FDA-led community wide MicroArray Quality Control (MAQC) consortium to analyze technical performance and practical utility of emerging genomics technologies with emphasis on regulatory application and precision medicine; (2) Development of Liver Toxicity Knowledge Base (LTKB) to address the drug safety concerns related to drug-induced liver injury (DILI); (3) Designing and developing computer based technology to support the FDA’s effort on bioinformatics and scientific computing (e.g., development of the FDA genomic tool, ArrayTrack, to support the pharmacogenomics data review in FDA); (4) Developing machine learning and AI for digital health and drug repositioning; and (5) Conducting molecular modeling and QSARs on various toxicological endpoints such as endocrine disruptor and carcinogenicity.  Dr. Tong has published over 300 peer-reviewed papers and book chapters.

Webcast Recording:

https://collaboration.fda.gov/pujh2p4d9d49/

For technical assistance please contact [email protected].

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