AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine. (28th February 2023)

• Record Type: Journal Article
• Authors: Quan, Xueping; Cai, Weijing; Xi, Chenghang; Wang, Chunxiao; Yan, Linghua
• Is Part Of: Database. Volume 2023(2023)
• Publication Date: 2023-02-28
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baad006
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: The development of high-throughput molecular testing techniques has enabled the large-scale exploration of the underlying molecular causes of diseases and the development of targeted treatment for specific genetic alterations. However, knowledge to interpret the impact of genetic variants on disease or treatment is distributed in different databases, scientific literature studies and clinical guidelines. AIMedGraph was designed to comprehensively collect and interrogate standardized information about genes, genetic alterations and their therapeutic and diagnostic relevance and build a multi-relational, evidence-based knowledge graph. Graph database Neo4j was used to represent precision medicine knowledge as nodes and edges in AIMedGraph. Entities in the current release include 30 340 diseases/phenotypes, 26 140 genes, 187 541 genetic variants, 2821 drugs, 15 125 clinical trials and 797 911 supporting literature studies. Edges in this release cover 621 731 drug interactions, 9279 drug susceptibility impacts, 6330 pharmacogenomics effects, 30 339 variant pathogenicity and 1485 drug adverse reactions. The knowledge graph technique enables hidden knowledge inference and provides insight into potential disease or drug molecular mechanisms. Database URL : http://aimedgraph.tongshugene.net:8201