利用预先训练的语言模型来挖矿微生物群与疾病的关系
Nikitha Karkera1, Sathwik Acharya2,3, Sucheendra K Palaniappan4,5,6
1SBX Corporation, Tokyo, Japan.
BMC bioinformatics
|July 19, 2023
概括
这项研究精心调整了语言模型,从科学文献中提取微生物与疾病的相互作用. 微调模型取得了最先进的结果,改善了微生物组研究的信息提取.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 自然语言处理自然语言处理.
背景情况:
- 人类微生物组显著影响健康,但在文献中,微生物与疾病的联系是分散的.
- 对微生物与疾病相互作用的结构化提取对于推进研究至关重要.
- 深度学习和NLP的进步为信息提取提供了新的途径.
研究的目的:
- 利用最先进的深度学习语言模型来提取微生物与疾病的关系.
- 评估和微调特定领域生物医学文本分析的语言模型.
主要方法:
- 在零射击和少数射击设置中评估了多个预训练的大型语言模型 (LLM).
- 精心调整的LLM包括GPT-3,BioGPT,BioMedLM,BERT,BioMegatron,PubMedBERT,BioClinicalBERT,以及BioLinkBERT. 这三种类型的LLM都具有很高的精度,包括GPT-3,BioGPT,BioMedLM,BERT,BioMegatron,PubMedBERT和BioLinkBERT.
- 使用标记训练数据来评估模型性能,用于微生物与疾病相互作用的提取.
主要成果:
- 现成的LLM表现不佳,凸显了对特定领域微调的需求.
- 精心调整的模型,特别是GPT-3,BioMedLM和BioLinkBERT,实现了最先进的性能.
- 取得的F1平均得分,精度和回忆力超过了以前的基准.
结论:
- 预先训练的语言模型是有效的转移学习者,当它们与特定领域的数据进行微调时.
- 微调使微生物群与疾病相互作用的最先进提取能够使用有限的数据.
- 这种方法提高了生物医学文献中关键信息的可访问性.
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