A knowledge graph to interpret clinical proteomics data

Alberto Santos, Ana R Colaço, Annelaura B Nielsen, Lili Niu, Maximilian Strauss, Philipp E Geyer, Fabian Coscia, Nicolai J Wewer Albrechtsen, Filip Mundt, Lars Juhl Jensen, Matthias Mann


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.

Original languageEnglish
JournalNature Biotechnology
Issue number5
Pages (from-to)692-702
Number of pages11
Publication statusPublished - May 2022


  • Algorithms
  • Decision Making, Computer-Assisted
  • Knowledge Bases
  • Machine Learning
  • Pattern Recognition, Automated
  • Precision Medicine/methods
  • Proteomics/standards


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