Development and Validation of an AI‐Driven System for Automatic Literature Analysis and Molecular Regulatory Network Construction

Autor: Jia Li, Hailin Zhang, Jiamin Wang, Mei Deng, Zhiyong Li, Wei Jiang, Kejin Xu, Lianlian Wu, Zehua Dong, Jun Liu, Qianshan Ding, Honggang Yu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Advanced Science, Vol 11, Iss 44, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 2198-3844
DOI: 10.1002/advs.202405395
Popis: Abstract Decoding gene regulatory networks is essential for understanding the mechanisms underlying many complex diseases. GENET is developed, an automated system designed to extract and visualize extensive molecular relationships from published biomedical literature. Using natural language processing, entities and relations are identified from a randomly selected set of 1788 scientific articles, and visualized in a filterable knowledge graph. The performance of GENET is evaluated and compared with existing methods. The named entity recognition model has achieved an overall precision of 94.23% (4835/5131; 93.56–94.84%), recall of 97.72% (4835/4948; 97.27–98.10%), and an F1 score of 95.94%. The relation extraction model has demonstrated an overall precision of 91.63% (2593/2830; 90.55–92.59%), recall of 89.17% (2593/2908; 87.99–90.25%), and an F1 score of 90.38%. GENET significantly outperforms existing methods in extracting molecular relationships (P
Databáze: Directory of Open Access Journals
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