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Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis
- Title
- Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis
- Authors
- Han; Seokjin; Lee; Ji Eun; Kang; Seolhee; So; Minyoung; Jin; Hee; Jang Ho; Baek; Sunghyeob; Jun; Hyungjin; Kim; Tae Yong; Yun-Sil
- Ewha Authors
- 이윤실
- SCOPUS Author ID
- 이윤실
![scopus](/images/layout/icon2.png)
- Issue Date
- 2024
- Journal Title
- Briefings in Bioinformatics
- ISSN
- 1467-5463
- Citation
- Briefings in Bioinformatics vol. 25, no. 2
- Keywords
- epithelial-mesenchymal transition; idiopathic pulmonary fibrosis; knowledge graph; neural network; target identification
- Publisher
- Oxford University Press
- Indexed
- SCIE; SCOPUS
![scopus](/images/layout/scopus2.gif)
- Document Type
- Article
- 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. © 2024 The Author(s).
- DOI
- 10.1093/bib/bbae035
- Appears in Collections:
- 약학대학 > 약학과 > Journal papers
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