评估GPT和BERT模型用于生物医学文本中蛋白质-蛋白质相互作用的识别
Hasin Rehana1,2, Nur Bengisu Çam3, Mert Basmaci3
1Department of Computer Science, School of Electrical Engineering & Computer Science, University of North Dakota, Grand Forks, ND 58202, United States.
Bioinformatics advances
|September 25, 2024
概括
检测蛋白质与蛋白质相互作用 (PPI) 对科学发现至关重要. GPT-4在从文本中识别PPI方面表现出强的表现,与BioBERT等专业模型相美.
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
- 自然语言处理自然语言处理.
背景情况:
- 蛋白与蛋白相互作用 (PPI) 是生物过程,疾病机制和药物开发的基础.
- 生物医学文献的快速增长需要自动化方法来提取PPI信息.
- 预训练语言模型 (PLM) 显示了在生物学中推进自动信息提取的潜力.
研究的目的:
- 评估基于变压器的语言模型在生物医学文本中检测蛋白质与蛋白质相互作用 (PPI) 的有效性.
- 在已建立的PPI提取数据集上比较像BioBERT和GPT-4这样的模型的性能.
主要方法:
- 评估了多个基于变压器的模型,包括BioBERT和GPT-4.
- 测试了三个黄金标准体的模型:逻辑学习语言,人类蛋白质参考数据库和交互提取性能评估.
- 使用精度,回忆和F1分数等指标评估性能.
主要成果:
- 双向编码器模型通常表现最好,BioBERT在逻辑数据集的学习语言中获得了高回忆率 (91.95%) 和F1得分 (86.84%).
- 在同一个数据集上,GPT-4表现出了竞争力的表现,获得了最高的精度 (88.37%) 和强大的F1得分 (86.49%).
- GPT-4的性能表明它在提取PPI的能力,即使没有特定的生物医学预训练.
结论:
- 基于变压器的模型,包括GPT-4,是用于自动检测蛋白质-蛋白质相互作用的有效工具.
- GPT-4提供了一个有前途的方法,从生物医学文献中挖掘PPI.
- 该研究强调了先进的NLP模型在加速生物研究和药物发现方面的潜力.
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