Structuring Biomedical Noise: Graphs, AI and the Drug Discovery Dilemma

  • Season 2 · Episode 10 of 11
  • Length: 36 min
Play video on YouTube: Structuring Biomedical Noise: Graphs, AI and the Drug Discovery Dilemma
Presented by Stefan Munker: more videos Stefan Munker on LinkedIn (opens in a new tab) Presenter
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  1. 1Using Ontologies to Guide Knowledge Graph Creation from Unstructured Data, duration 1:12:30
  2. 2Using Ontologies to Guide Knowledge Graph Creation, duration 54:53
  3. 3Blueprints for Knowledge Graph Construction from Unstructured Data, duration 1:06:12
  4. 4Ontology-driven end-to-end GraphRAG with the GraphRAG Python Package, duration 1:09:29
  5. 5One Ontology to Rule Them All: Building Knowledge Graphs from Mixed Data, duration 1:06:38
  6. 6Retrieval Methods Compared, duration 1:03:36
  7. 7Enhancing LLM Tool Calling with Ontologies, duration 53:19
  8. 8Agentic Workflows for Dynamic Ontology Selection in KG Construction, duration 1:06:41
  9. 9Structural Patterns for Ontology Reconciliation, duration 1:03:36
  10. 10Structuring Biomedical Noise: Graphs, AI and the Drug Discovery Dilemma, duration 35:42
  11. 112 years of KGs and GenAI. A Recap, duration 57:35

Season 02 Episode 10 of Going Meta – a Series on Semantics, Knowledge Graphs and All Things AI
Topic: Structuring Biomedical Noise: Graphs, AI and the Drug Discovery Dilemma

Together with our guest, Stefan Munker, we discuss in this episode how unstructured biomedical data is inflating drug development costs and how graph-powered AI could be the key to unlocking more predictive, translationally relevant insights. Can better data structures cure what ails pharmaceutical R&D?

Oncera is spearheading AI in life sciences, bridging the gap between clinics, drug discovery, and clinical implementation.

Resources mentioned

GenAI & GraphRAGKnowledge graphs

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