Knowledge graphs and more: Analytics-driven drug discovery using advanced biomedical relationships knowledge
About the session
High-quality biomedical relationships knowledge is the cornerstone of modern and innovative data- and analytics-driven drug discovery. Yet this knowledge is locked in thousands of publications and dozens of databases. This session will show you how to unlock this knowledge and use it to strengthen your efforts in data science-driven drug discovery.
In this session, you'll learn about:
- High-quality biomedical relationships knowledge – what it is and how to access it
- Knowledge graphs and knowledge graph analysis
- Artificial intelligence (AI)-driven target identification and drug repositioning using knowledge graphs and biomedical relationships
- Disease subtyping and biomarker identification based on functional features
- Target, disease and drug intelligence portals: Application development and data integration leveraging biomedical relationships
Don't miss this opportunity to discover how to give your drug discovery programs a data science-driven advantage by leveraging high-quality biomedical relationships knowledge.
Dr. Marina Bessarabova holds an M.B.A. and a Ph.D. in biological sciences. She is the global product manager for QIAGEN Digital Insights data science solutions. Before QIAGEN Digital Insights, she was Head of Product Management, Discovery and Data Science at Informa. Prior to that, she led the discovery and translational services practice at Clarivate Analytics.
Dr. Andreas Kraemer is a computational biologist at QIAGEN Digital Insights, leading the development of machine learning and knowledge graph-based algorithms for discovery and clinical products. He joined QIAGEN in 2014 with the acquisition of Ingenuity Systems, where he worked in a similar capacity. Prior to that, Dr. Kraemer held positions at IBM and Schott (Germany) where he conducted research and development in cheminformatics and molecular simulations. He has a Ph.D. in Theoretical Physics.