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

Seokjin Han, Ji Eun Lee, Seolhee Kang, Minyoung So, Hee Jin, Jang Ho Lee, Sunghyeob Baek, Hyungjin Jun, Tae Yong Kim, Yun Sil Lee

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numberbbae035
JournalBriefings in Bioinformatics
Volume25
Issue number2
DOIs
StatePublished - 1 Mar 2024

Bibliographical note

Publisher Copyright:
© 2024 The Author(s).

Keywords

  • epithelial-mesenchymal transition
  • idiopathic pulmonary fibrosis
  • knowledge graph
  • neural network
  • target identification

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