<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Santos A</submitter><funding>EC | Horizon 2020 Framework Programme</funding><funding>Novo Nordisk Foundation Center for Protein Research</funding><funding>Novo Nordisk Fonden</funding><funding>Max-Planck-Gesellschaft</funding><pagination>692-702</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9110295</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>40(5)</volume><pubmed_abstract>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 in</pubmed_abstract><journal>Nature biotechnology</journal><pubmed_title>A knowledge graph to interpret clinical proteomics data.</pubmed_title><pmcid>PMC9110295</pmcid><funding_grant_id>NNF14CC0001</funding_grant_id><funding_grant_id>PI Lars Juhl Jensen</funding_grant_id><funding_grant_id>PI Simon Rasmussen</funding_grant_id><funding_grant_id>PI Matthias Mann</funding_grant_id><funding_grant_id>846795</funding_grant_id><funding_grant_id>NNF15CC0001</funding_grant_id><pubmed_authors>Santos A</pubmed_authors><pubmed_authors>Albrechtsen NJW</pubmed_authors><pubmed_authors>Mundt F</pubmed_authors><pubmed_authors>Strauss M</pubmed_authors><pubmed_authors>Niu L</pubmed_authors><pubmed_authors>Geyer PE</pubmed_authors><pubmed_authors>Colaco AR</pubmed_authors><pubmed_authors>Coscia F</pubmed_authors><pubmed_authors>Jensen LJ</pubmed_authors><pubmed_authors>Mann M</pubmed_authors><pubmed_authors>Nielsen AB</pubmed_authors></additional><is_claimable>false</is_claimable><name>A knowledge graph to interpret clinical proteomics data.</name><description>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 in</description><dates><release>2022-01-01T00:00:00Z</release><publication>2022 May</publication><modification>2026-05-09T23:37:21.622Z</modification><creation>2025-02-19T02:58:50.28Z</creation></dates><accession>S-EPMC9110295</accession><cross_references><pubmed>35102292</pubmed><doi>10.1038/s41587-021-01145-6</doi></cross_references></HashMap>