From virtual patients to biomedical knowledge graphs and back

Organized & sponsored by Team VPE in partnership with

Goal

Machine learners and computational biologists unite as we seek to unify the representation between virtual patient technology and biomedical knowledge graph technology so that virtual patients can be automatically generated from biomedical knowledge graphs

Key points

  • Virtual patients and biomedical knowledge graphs are disconnected technologies developed by different people with different objectives and different expertise’s
  • We will leverage the technologies of both worlds to bridge the divide and enable seamless generation of virtual patients from biomedical knowledge graphs
  • Teams composed of people experienced in data science and machine learning, and computational biology will compete with the guidance of industry and research experts

Why participate?

  • Engage with top experts in machine learning, computational biology, and biomedical AI. Connect with industry leaders, researchers, and like-minded innovators.
  • Receive a formal certificate recognizing your participation and contribution—great for your professional portfolio and LinkedIn profile.
  • Your work will contribute to open-source projects, ensuring visibility and impact in the global research community.
  • Outstanding contributions may lead to co-authored publications or further collaboration opportunities with academia and industry.
  • Tackle a cutting-edge problem at the intersection of AI and biomedicine, influencing the future of virtual patient technology.
  • Stand out to potential employers, research institutions, and industry partners who are actively looking for talent in AI-driven biomedical research.
  • Learn directly from experts in computational biology, machine learning, and biomedical AI. Gain hands-on experience with state-of-the-art tools.

All outputs of the Hackathon will be open-source and eventually published

Free Spot available! Register here

Learn more about BioMed X

Team VPE

Team VPE

The goal of team VPE is to develop a versatile computational platform that can predict the efficacy of first- or best-in-class drug candidates in virtual patient populations at an unprecedented accuracy, thereby addressing one of the most critical bottlenecks of the pharmaceutical industry today: a 90% failure rate of new drug candidates during clinical development.

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