Fig. 1: The clinical knowledge graph architecture.

a, The CKG architecture is implemented in Python and contains several independent modules responsible for connecting to the graph database (graphdb_connector), building the graph (graphdb_builder), analyzing and visualizing experimental data (analytics_core), displaying and launching multiple applications (report_manager); it also contains a repository of Jupyter notebooks with analysis examples (notebooks). The code is accessible at https://github.com/MannLabs/CKG or as a complete Docker container. b, The CKG analytics core implements multiple up-to-date data science algorithms for statistical analysis and visualization of proteomics data: data preparation, exploration, analysis and visualization. This library can also be used directly within Jupyter notebooks, independently of the other CKG modules, and to analyze other omics types. c, The CKG graph database data model was designed to integrate multi-level clinical proteomics experiments and to annotate them with biomedical data. It defines different nodes (for example, Protein, Metabolite and Disease) and the types of relationship connecting them (for example, HAS_PARENT and HAS_QUANTIFIED_PROTEIN). FC, fold change; Src, source code.