评估基于GPT和BERT的模型,用于识别生物医学文本中的蛋白质蛋白相互作用
Hasin Rehana1, Nur Bengisu Çam2, Mert Basmaci2
1Computer Science Graduate Program, University of North Dakota, Grand Forks, North Dakota, 58202, USA.
ArXiv
|May 20, 2024
概括
像BERT和GPT-4这样的大型语言模型显示出在生物医学文本中识别蛋白质与蛋白质相互作用 (PPI) 的前景. 总体而言,BERT模型表现最好,但GPT-4在PPI检测方面也表现出强大的能力.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 计算生物学 计算生物学
背景情况:
- 精确检测蛋白质与蛋白质相互作用 (PPI) 对理解生物过程,疾病和药物开发至关重要.
- 越来越多的生物医学文献需要自动化方法来提取PPI以加快科学发现.
- 预训练语言模型 (PLM) 为自然语言处理 (NLP) 任务提供了高级功能.
研究的目的:
- 评估各种生成预训练变压器 (GPT) 和变压器双向编码器表示 (BERT) 模型在识别科学文献中的PPI方面的性能.
- 为了比较不同PLM的有效性,使用已建立的,手动策划的PPI提取数据集.
主要方法:
- 评估了多个BERT和GPT模型在三个黄金标准体上:逻辑学习语言 (LLL),人类蛋白质参考数据库 (HPRD) 和交互提取性能评估 (IEPA).
- 根据PPI识别的精度,回忆和F1得分来评估模型性能.
主要成果:
- 基于BERT的模型,特别是BioBERT和PubMedBERT,在PPI检测方面表现出卓越的性能.
- 生物BERT获得了最高的回忆 (91.95%) 和F1得分 (86.84%),而PubMedBERT获得了最高的精度 (85.25%).
- 在LLL数据集上,GPT-4表现出具有竞争力的性能,与顶级BERT模型相美,精度为88.37%,回忆率为85.14%,F1得分为86.49%.
结论:
- 无论是BERT还是GPT模型都有效地检测生物医学文本中的蛋白质与蛋白质相互作用.
- GPT模型显示了生物医学文献挖掘的巨大潜力,即使没有专门的生物医学数据预先培训.
- 对微调这些模型的进一步研究可以提高它们在专门的生物医学NLP任务中的实用性.
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