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Brief. Bioinform., Jan 22 2024, 25 (2) bbae035 | https://doi.org/10.1093/bib/bbae035
Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis
Seokjin Han 1†, Ji Eun Lee2†, Seolhee Kang1, Minyoung So1, Hee Jin2, Jang Ho Lee1, Sunghyeob Baek1, Hyungjin Jun1, Tae Yong Kim1 and Yun-Sil Lee2
1Standigm Inc., Nonhyeon-ro 85-gil, 06234, Seoul, Republic of Korea
2College of Pharmacy, Ewha Womans University, Ewhayeodae-gil, 03760, Seoul, Republic of Korea
†Seokjin Han and Ji Eun Lee contributed equally to this work.
Corresponding authors : Seokjin Han, Yun-Sil Lee
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.
논문정보
- Research article
- 2024년 01월 (BRIC 등록일 2024-02-14)
- 국내 연구진
- 의약학 > 응용의학
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