A knowledge graph to interpret clinical proteomics data

• Published in: Nature biotechnology Vol. 40; no. 5; pp. 692 - 702
• Main Authors: Santos, Alberto, Colaço, Ana R., Nielsen, Annelaura B., Niu, Lili, Strauss, Maximilian, Geyer, Philipp E., Coscia, Fabian, Albrechtsen, Nicolai J. Wewer, Mundt, Filip, Jensen, Lars Juhl, Mann, Matthias
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.05.2022; Nature Publishing Group
• Subjects: 631/114/2164; 631/114/2401; 631/553/794; 631/61/475; 692/53; Agriculture; Algorithms; Bioinformatics; Biomarkers; Biomedical and Life Sciences; Biomedical data; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Clinical decision making; Data integration; Decision making; Decision Making, Computer-Assisted; Integration; Knowledge Bases; Knowledge representation; Life Sciences; Machine Learning; Nodes; Pattern Recognition, Automated; Precision medicine; Precision Medicine - methods; Precision Medicine - standards; Proteomics; Proteomics - standards; Proteomics - statistics & numerical data
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-021-01145-6

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making. A knowledge graph platform integrates proteomics with other omics data and biomedical databases.