Computational Drug Repurposing in the Era of Artificial Intelligence

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In an era where medical innovation is more critical than ever, artificial intelligence (AI) offers unprecedented opportunities in the realm of healthcare, particularly in drug discovery and development. Harnessing AI for drug repurposing represents a frontier in accelerating the identification of new therapeutic uses for existing drugs, reducing time and costs associated with traditional drug development.

In this talk, we will explore the transformative potential of computational drug repurposing in the era of AI. We will delve into the applications of deep learning for drug-target prediction and drug sensitivity prediction, as well as the utilization of knowledge graphs to uncover hidden relationships between drugs and biological entities. Additionally, we will introduce the large language model (LLM) and discuss its potential in processing vast amounts of biomedical literature and clinical trial data to inform drug repurposing efforts. By highlighting real-world examples and case studies, this talk aims to provide a comprehensive overview of the opportunities and challenges that lie ahead in the field of AI-driven drug repurposing.

Join us to delve into how computational drug repurposing, powered by AI, can become a pivotal tool in swiftly and effectively responding to future healthcare challenges.

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  Date and Time

  Location

  Hosts

  Registration



  • Date: 13 Feb 2024
  • Time: 06:00 PM to 07:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
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Online via Zoom:
https://lehigh.zoom.us/j/98220036426

All are welcome. You must register, but you do not have to be an IEEE member to attend.

  • Contact Event Hosts
  • Starts 08 December 2023 04:20 PM
  • Ends 13 February 2024 06:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • No Admission Charge


  Speakers

Nansu Zong Nansu Zong of Mayo Clinic

Topic:

Computational Drug Repurposing in the Era of Artificial Intelligence

In an era where medical innovation is more critical than ever, artificial intelligence (AI) offers unprecedented opportunities in the realm of healthcare, particularly in drug discovery and development. Harnessing AI for drug repurposing represents a frontier in accelerating the identification of new therapeutic uses for existing drugs, reducing time and costs associated with traditional drug development. In this talk, we will explore the transformative potential of computational drug repurposing in the era of AI. We will delve into the applications of deep learning for drug-target prediction and drug sensitivity prediction, as well as the utilization of knowledge graphs to uncover hidden relationships between drugs and biological entities. Additionally, we will introduce the large language model (LLM) and discuss its potential in processing vast amounts of biomedical literature and clinical trial data to inform drug repurposing efforts. By highlighting real-world examples and case studies, this talk aims to provide a comprehensive overview of the opportunities and challenges that lie ahead in the field of AI-driven drug repurposing.

Biography:

Dr. Nansu Zong is Senior Associate Consultant, and Assistant Professor of Biomedical Informatics of the Department of Artificial Intelligence and Informatics, Mayo Clinic. He has also served as the Adjunct Professor at the University of Minnesota and  Vice Chair of the Knowledge Representation Working Group, AMIA. Dr. Zong's lab leverages AI and biomedical knowledge to create advanced AI-based predictive models. His research team is at the forefront of developing cutting-edge AI techniques for computational drug repurposing and predicting treatment outcomes using electronic health records (EHRs).

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