Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis

Abstract Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms—a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an atte...

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Bibliographic Details
Published in:Briefings in bioinformatics 2024-01, Vol.25 (2)
Main Authors: Han, Seokjin, Lee, Ji Eun, Kang, Seolhee, So, Minyoung, Jin, Hee, Lee, Jang Ho, Baek, Sunghyeob, Jun, Hyungjin, Kim, Tae Yong, Lee, Yun-Sil
Format: Article
Language:English
Subjects:
Artificial Intelligence
Case Study
Drug development
Fibrosis
Gene expression
Genes
Graph theory
Humans
Idiopathic Pulmonary Fibrosis - drug therapy
Idiopathic Pulmonary Fibrosis - genetics
Knowledge representation
Lung - metabolism
Lung diseases
Neural networks
Pattern Recognition, Automated
Therapeutic targets
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Summary:Abstract Standigm ASK™ revolutionizes healthcare by addressing the critical challenge of identifying pivotal target genes in disease mechanisms—a fundamental aspect of drug development success. Standigm ASK™ integrates a unique combination of a heterogeneous knowledge graph (KG) database and an attention-based neural network model, providing interpretable subgraph evidence. Empowering users through an interactive interface, Standigm ASK™ facilitates the exploration of predicted results. Applying Standigm ASK™ to idiopathic pulmonary fibrosis (IPF), a complex lung disease, we focused on genes (AMFR, MDFIC and NR5A2) identified through KG evidence. In vitro experiments demonstrated their relevance, as TGFβ treatment induced gene expression changes associated with epithelial–mesenchymal transition characteristics. Gene knockdown reversed these changes, identifying AMFR, MDFIC and NR5A2 as potential therapeutic targets for IPF. In summary, Standigm ASK™ emerges as an innovative KG and artificial intelligence platform driving insights in drug target discovery, exemplified by the identification and validation of therapeutic targets for IPF.
ISSN:1467-5463
1477-4054
DOI:10.1093/bib/bbae035