从生物医学文献中自动提取细菌的转录调节相互作用,使用基于BERT的方法
Alfredo Varela-Vega1, Ali-Berenice Posada-Reyes2, Carlos-Francisco Méndez-Cruz1
1Programa de Genómica Computacional, Centro de Ciencias Genómicas, UNAM, Av. Universidad S/N Col. Chamilpa, Cuernavaca, Morelos 62210, México.
Database : the journal of biological databases and curation
|August 30, 2024
概括
本研究介绍了一种基于BERT的方法,可以自动从科学文献中提取细菌转录调节网络 (TRN),提高研究人员的完整性和效率.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 转录调节网络 (TRNs) 对于理解细菌对环境信号的反应至关重要.
- 现有的TRN通常是不完整的,并且用于TRN提取的手册文献策划是耗时的.
- 需要自动化方法来解决TRN重建的局限性.
研究的目的:
- 开发和评估一种用于从生物医学文献中提取细菌转录调节相互作用的新方法.
- 为了利用双向变压器语言理解 (BERT) 架构进行自动TRN提取.
- 帮助重建细菌和其他生物的综合TRN.
主要方法:
- 利用基于BERT的模型进行微调,用于分类转录性调节相互作用.
- 应用转移学习适应预先训练的语言模型,用于TRN提取的特定任务.
- 对大肠杆菌和沙门氏菌的数据集评估了模型的性能.
主要成果:
- 在对大肠杆菌的转录调节相互作用进行分类方面取得了高性能 (F1-Score: 0.8685).
- 从264个沙门氏菌的文章中成功提取了82%的转录调节网络.
- 在现有精选数据中不存在的新型相互作用被确定.
结论:
- 基于BERT的方法显著提高了从文献中提取TRN的效率和准确性.
- 这种方法提供了一个可扩展的解决方案,以克服手工策划和不完整的TRN的局限性.
- 开发的方法作为重建TRN在各种生物环境的基础.
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