The QIAGEN Biomedical Knowledge Base (QIAGEN BKB) API will allow researchers to more easily use knowledge graphs, artificial intelligence (AI) and machine learning (ML) to accelerate research into disease mechanisms and subtypes, targets and biomarkers, mechanisms of action and other areas. It will also reduce infrastructure costs for hosting and maintaining QIAGEN’s huge archive of manually curated data.

“One of the greatest challenges facing researchers today is accessing centralized, structured, normalized and high-quality data to drive data-science projects,” said Dr. Jonathan Sheldon, Senior Vice President of QIAGEN Digital Insights. “Our new API taps into the power of AI and data science by enabling easy and flexible query and export of QIAGEN BKB into data warehouses and projects. API access helps researchers push beyond the limitations of the human mind, time and purpose-built applications to enable the discovery of novel disease and drug insights.”

The QIAGEN BKB API provides direct access to the same high-quality, human-reviewed data that fuels the widely used QIAGEN Ingenuity Pathway Analysis (IPA) application, which has been used by over 40,000 scientists in pharmaceuticals, biotech and academia over the past 20 years and cited in over 57,000 scientific papers. The knowledge base structures and integrates causal biomedical relationships between genes, diseases, functions, targets, drugs, chemicals and other objects to enable the prediction and validation of novel target-disease and drug-disease relationships. The new API streamlines how data scientists can query, integrate, structure, train and interpret the data into new and existing projects.

QIAGEN BKB collects, structures and integrates information about biomedical relationships locked in thousands of publications and dozens of databases. The API will allow researchers to harness this trove more easily and quickly – and to either purchase the entire knowledge or only more targeted data, making it more attractive for smaller organizations that require smaller data volumes. Through its flexibility and granularity, the API creates new opportunities to extract profound insights to biological questions.