TY - JOUR T1 - Clinical Knowledge Graph Integrates Proteomics Data into Clinical Decision-Making JF - bioRxiv DO - 10.1101/2020.05.09.084897 SP - 2020.05.09.084897 AU - Alberto Santos AU - Ana R. Colaço AU - Annelaura B. Nielsen AU - Lili Niu AU - Philipp E. Geyer AU - Fabian Coscia AU - Nicolai J Wewer Albrechtsen AU - Filip Mundt AU - Lars Juhl Jensen AU - Matthias Mann Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/05/10/2020.05.09.084897.abstract N2 - 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. ER -