结合手动注释和深度学习自然语言处理研究,研究遗传性疾病相关生物医学文献中的精确实体提取
Dao-Ling Huang1,2, Quanlei Zeng3, Yun Xiong3
1BGI Research, Shenzhen, 518083, China. dlhuang1217@gmail.com.
Interdisciplinary sciences, computational life sciences
|February 10, 2024
概括
这项研究开发了深度学习模型,用于在遗传性疾病文献中准确地提取实体. 这些模型获得了高的F1分数,特别是首次实现了变体实体提取.
科学领域:
- 生物医学信息学 生物医学信息学
- 基因组学就是基因组学.
- 自然语言处理自然语言处理.
背景情况:
- 从生物医学文献中准确地提取实体对于理解遗传性疾病至关重要.
- 现有的方法可能无法完全捕捉复杂的遗传信息,特别是变异.
研究的目的:
- 在遗传性疾病文献中开发和评估用于精确实体提取 (基因,变异,疾病,物种) 的深度学习模型.
- 使用先进的NLP技术,建立一个用于变异实体提取的基准.
主要方法:
- 400篇全文文章的手动注释由遗传学专家进行.
- 基于BERT和基于DistilBERT的命名实体识别 (NER) 模型的培训和微调.
- 使用雅卡德指数和F1分数进行绩效评估.
主要成果:
- 手动注释为四种实体类型实现了0.866的Jaccard指数.
- 基于BERT的NER模型获得了高F1分数:基因 (97.28%),变异 (93.52%),疾病 (92.54%),物种 (95.76%).
- 基于DistilBERT的NER模型也表现出强的表现,变体提取实现了与最先进模型相似的结果.
结论:
- 深度学习模型,特别是基于BERT的NER,在遗传性疾病文献中显示出对实体提取的高准确性.
- 这项研究首次使用大型语言模型成功提取了变体实体,实现了竞争性表现.
- 开发的方法提高了处理和分析生物医学文献用于遗传研究的能力.
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