• QIAGEN Biomedical KB-AI contains over 640 million biomedical relationships, including gene, disease and drug causal relationships, to aid in data-driven drug discovery

• AI-driven insights complement human-curated QIAGEN Biomedical KB-HD and help identify novel relationships between diseases, biological pathways and molecular interactions that may be missed by traditional methods

• Workflow cuts down on hypothesis testing time and speeds up the identification of promising therapeutic avenues with clear, structured data

QIAGEN Biomedical KB-AI is built on a massive dataset of biomedical literature and other scientific sources. It identifies and extracts causal relationships between genes, diseases, drugs and other biological entities with AI, generating over 600 million more biomedical relationships than its complement, QIAGEN Biomedical KB-HD. This expansive knowledge base helps data scientists understand disease mechanisms, identify drug targets or biomarkers and explore strategies for repurposing existing drugs. QIAGEN Biomedical KB-AI provides the most complete picture of biomedical relationships, including edge cases and novel relationships.

While QIAGEN Biomedical KB-HD is manually curated and known for its high quality and accuracy, QIAGEN Biomedical KB-AI contains over 25x more relationships, allowing data scientists to generate new insights. Both knowledge bases can be used to provide both high-quality and high-quantity biomedical relationships data for data scientists and bioinformaticians to mine and validate.

“QIAGEN Biomedical KB-AI represents a significant step forward in our mission to empower biopharma customers with the most comprehensive and informative molecular knowledge bases,” said Jonathan Sheldon, Senior Vice President of QIAGEN Digital Insights. “By combining the strengths of AI and human curation approaches, we provide researchers with the widest, deepest and highest quality knowledge sources.”

Key features of QIAGEN Biomedical KB-AI include: 

• Massive scale: 640 million biomedical relationships curated from literature, patents, grants and other sources
• Causality: Over 6.4 million gene causal relationships, 1.99 million disease causal relationships and 1.16 million drug causal relationships
• Structured: Results are structured in an ontology for rapid querying and advanced analytics
• Timely: Updated quarterly to capture the most recent research and discoveries 

QIAGEN continuously integrates AI technology in its QIAGEN Digital Insights portfolio. As recently as September 2023, it released an AI-driven enhancement to its market-leading QIAGEN Clinical Insight Interpret product capabilities.