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 interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.
Highlights
The paradigm of evidence-based precision medicine has evolved toward a more comprehensive analysis of disease phenotypes
The resulting flexible structure, called a knowledge graph, quickly adapts to complex data with their relationships and enables the efficient use of network analysis techniques to identify hidden patterns and knowledge[13,17–19]. We take this concept into a new direction and describe a knowledge graph framework that facilitates harmonization of proteomics with other omics data while integrating the relevant biomedical databases and text extracted from scientific publications
Articles by integrating available data from a range of publicly accessible databases, user-conducted experiments, existing ontologies and scientific publications; (3) connect and query this graph database; and (4) facilitate data visualization, repository and analysis via online reports and Jupyter notebooks (Fig. 1a,b). This architecture seamlessly harmonizes and integrates data as well as user-supplied analysis. It facilitates data sharing and visualization as well as interpretation based on detailed statistical reports annotated with biomedical knowledge, generating clinically relevant results
Summary
The paradigm of evidence-based precision medicine has evolved toward a more comprehensive analysis of disease phenotypes This requires seamless integration of diverse data, such as clinical, laboratory, imaging and multiomics data (genomics, transcriptomics, proteomics or metabolomics)[1]. The resulting flexible structure, called a knowledge graph, quickly adapts to complex data with their relationships and enables the efficient use of network analysis techniques to identify hidden patterns and knowledge[13,17–19] We take this concept into a new direction and describe a knowledge graph framework that facilitates harmonization of proteomics with other omics data while integrating the relevant biomedical databases and text extracted from scientific publications. Termed the CKG, it constitutes a graph database of millions of nodes and relationships It allows clinically meaningful queries and advanced statistical analyses, enabling automated data analysis, knowledge mining and visualization. Reproducible and transparent analysis in both standard workflows and interactive exploration based on Jupyter notebooks
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