Knowledge Graph Powered Human Proteome Knowledge Annotation and Knowledge Exploration Study
Proteome knowledge annotation facilitates the derivation of scientific hypotheses from existing knowledge.However,traditional annotation approaches are often not comprehensive and lack systemic integration,being limited to knowledge retrieval and aggregation.In this paper,a novel method involving knowledge graphs is proposed to integrate biomedical knowledge from 13 biomedical ontologies and databases.The knowledge graph,Biomedical Knowledge Graph(BMKG),was constructed with the graph database Neo4j.Metapaths were designed to create knowledge annotation schemes which incorporated prior knowledge with graph algorithms such as centrality measures.By leveraging similarity calculations,link prediction algorithms,and node2vec graph embedding,knowledge exploration analysis was facilitated.BMKG encompasses 2 508 348 nodes of 9 types and 25 362 594 relationships of 20 types.The BMKG knowledge annotation scheme facilitates diverse perspectives and multi-level annotation,which is demonstrated by its application to renal cell carcinoma tissue proteome data in annotating various biological aspects comprehensively,such as pathways,drugs,and phenotypes.Additionally,BMKG supports knowledge exploration studies,such as drug-disease association prediction,and the clustering of disease knowledge exhibits strong concordance with the Mondo ontology structure.Moreover,an online platform(http://bmkg.bmicc.org)has been established,with three analysis modules:knowledge retrieval,knowledge annotation,and knowledge analysis.Collectively,this study demonstrates the potential of knowledge graph approaches to enhance human proteome knowledge annotation and knowledge exploration.
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