{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Qin G"],"funding":["NCATS NIH HHS"],"pubmed_abstract":["As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy. Effective transformation of data into information requires harmonization of stakeholder goals, implementation, enforcement of standards regarding quality and completeness, and availability of resources for maintenance and updates. Systems such as the Biomedical Data Translator leverage knowledge graphs (KGs), structured and machine learning readable knowledge representation, to encode knowledge extracted through inferen"],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2024.11.14.623648"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11601480"],"repository":["biostudies-literature"],"pubmed_title":["Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data."],"pmcid":["PMC11601480"],"funding_grant_id":["OT2 TR003428","OT2 TR003441","OT2 TR003443","OT2 TR003445","OT2 TR003449"],"pubmed_authors":["Glusman G","Shmulevich I","Xu C","Joshi A","Roach JC","Goetz SL","Moxon ST","Ralevski A","Belhu B","Narsinh K","Morris ED","Roper R","Yao Y","Wei Q","Zhang Y","Hadlock J","Qin G","Glen AK","Brush MH"],"additional_accession":[]},"is_claimable":false,"name":"Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data.","description":"As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy. Effective transformation of data into information requires harmonization of stakeholder goals, implementation, enforcement of standards regarding quality and completeness, and availability of resources for maintenance and updates. Systems such as the Biomedical Data Translator leverage knowledge graphs (KGs), structured and machine learning readable knowledge representation, to encode knowledge extracted through inferen","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-04-07T21:06:04.408Z","creation":"2025-04-06T17:31:34.562Z"},"accession":"S-EPMC11601480","cross_references":{"pubmed":["39605475"],"doi":["10.1101/2024.11.14.623648"]}}