Leading biomedical relationships knowledge to drive innovation, confidently and efficiently
Biomedical relationships knowledge is required for innovative data- and analytics-driven drug discovery. It powers biomedical knowledge graph analysis, artificial intelligence (AI)-driven target identification and many more applications.
However, biomedical knowledge is locked in thousands of publications and dozens of databases. Collecting, structuring and integrating this knowledge is extremely challenging, as well as time- and resource-consuming.
We address these challenges with QIAGEN Biomedical Knowledge Base, the leading knowledge about biomedical relationships, manually structured and integrated from thousands of sources by experts.
QIAGEN Biomedical Knowledge Base fuels QIAGEN Ingenuity Pathway Analysis (IPA), our premier ‘omics data analysis and interpretation software. This is data you know well, and now you can access it directly.
Biomedical knowledge graph construction and analysis
Analytics- and AI-driven target identification and drug repositioning
Target, disease and drug intelligence portals
Disease subtype and biomarker identification based on functional features
High quality: Use accurate and industry-validated data.
Novel: Explore and analyze integrated knowledge about causal relationships, that are enriched with full context, between genes, diseases, drugs, targets, functions, toxicological processes and more, to produce novel discoveries otherwise hidden among siloed and diverse data.
Quick and efficient: Focus on answering questions, not gathering, structuring, disambiguating and cleaning up data. Our integrated knowledge enables you to automatically identify and prioritize hypotheses within minutes using biomedical knowledge graph analysis, AI and more.
Generated your way: Direct data access enables highly flexible exploration and analysis.
Accurate. This is data you can trust. It is produced using manual curation, including strict QC, and thus supports the highest quality requirements. Human-certified data goes in, and valuable discoveries come out.
Industry validated. For more than twenty years, the data has been used by leading organizations to successfully generate R&D discoveries. The data has been cited in tens of thousands of articles.
Multiple entity types, not only molecules. Includes relationships not only between molecules, but also between drugs, targets, diseases, functions, toxicological processes and more. This enables you to predict and validate novel target-disease and drug-disease relationships, among others.
Causal. Delivers causal biomedical relationships enabling novel and otherwise concealed discoveries.
Full context. Relationships are captured with full context, such as cellular and tissue location and organism, enabling insights specific to a particular question. Full context can only be delivered through manual curation, since multiple parts of an article are required to capture it.
Entity annotation. Entities are mapped to public identifiers and synonyms to support integration with other data sources.
Combine our leading data with your innovative analysis approaches and a wide range of advanced algorithms developed by the industry to power analytics- and AI-driven drug discovery.
Use the data within your own analysis and data-exploration applications.
Integrate the data with other data types and sources, as well as third-party technologies.
See for yourself how QIAGEN Biomedical Knowledge Base will enable you to leverage biomedical knowledge graph analysis, fuel your data- and analytics-driven drug discovery and transform your research.
We use our leading data, advanced analytics and AI to help answer your scientific questions and build custom applications
Discover how our biomedical knowledge graph can help accelerate your data science-driven drug discovery