Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls

Jeynes, J. Charles G.; James, Tim; Corney, Matthew

doi:10.1007/978-1-0716-3449-3_10

Cited by 1 publication

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data quality issues and potential security risks have been continuously emphasized in the KG application [ 95–97 , 99 , 100 ]. The multifaceted issues of data quality in data extraction and curation, bias, data poisoning and dataset update have been considered substantially by us and others and mitigated by diverse solutions, including the new NLP technology development [ 101 , 102 ], domain experts assignment [ 103 ], stepwise bias-mitigating framework [ 104 ], adversarial training [ 105 ] and automatic updating system for primary dataset sources, respectively. The ontology problems, such as acronyms, homonyms and the hierarchy of biomedical terminology, have been improved, but we still encounter problems in KG-based models, requiring a unified multimodal biomedical ontology system for ML [ 101 , 106 , 107 ].…”

Section: Discussionmentioning

confidence: 99%

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

Han,

Lee,

Kang

et al. 2024

Briefings in Bioinformatics

View full text Add to dashboard Cite

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.

show abstract