RT Journal Article
SR Electronic
T1 Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.09.084897
DO 10.1101/2020.05.09.084897
A1 Alberto Santos
A1 Ana R. Colaço
A1 Annelaura B. Nielsen
A1 Lili Niu
A1 Philipp E. Geyer
A1 Fabian Coscia
A1 Nicolai J Wewer Albrechtsen
A1 Filip Mundt
A1 Lars Juhl Jensen
A1 Matthias Mann
YR 2020
UL http://biorxiv.org/content/early/2020/05/10/2020.05.09.084897.abstract
AB The promise of precision medicine is to deliver personalized treatment based on the unique physiology of each patient. This concept was fueled by the genomic revolution, but it is now evident that integrating other types of omics data, like proteomics, into the clinical decision-making process will be essential to accomplish precision medicine goals. However, quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across myriad biomedical databases and publications makes this exceptionally difficult. To address this, we developed the Clinical Knowledge Graph (CKG), an open source platform currently comprised of more than 16 million nodes and 220 million relationships to represent relevant experimental data, public databases and the literature. The CKG also incorporates the latest statistical and machine learning algorithms, drastically accelerating analysis and interpretation of typical proteomics workflows. We use several biomarker studies to illustrate how the CKG may support, enrich and accelerate clinical decision-making.Competing Interest StatementThe authors have declared no competing interest.