将生物样本数据注释到标准本体学中,并得到大型语言模型的支持
Andrea Riquelme-García1, Juan Mulero-Hernández1, Jesualdo Tomás Fernández-Breis1
1Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, IMIB-Pascual Parrilla, Murcia, 30100, Spain.
Computational and structural biotechnology journal
|June 13, 2025
概括
精心调整的大型语言模型 (LLM) 显示了通过将本体识别符分配到样本标签上来实现生物数据注释的自动化,从而提高了数据集成的准确性和效率.
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
- 数据科学数据科学数据科学
背景情况:
- 生物数据的整合是具有挑战性的,因为不同的来源和差的语义正式化.
- 将数据映射到本体学中需要大量的手工工作,这阻碍了大规模分析.
- 大型语言模型 (LLM) 提供了自动化复杂语言任务的潜力,例如生物数据注释.
研究的目的:
- 评估各种大型语言模型 (LLM) 的有效性,包括基本和微调的GPT模型,用于自动分配生物样本标签的本体识别符.
- 使用已建立的生物本体学说,将LLM绩效与最先进的工具 (text2term) 进行比较.
主要方法:
- 使用LLM (基本和微调GPT模型) 来注释生物样品标签.
- 标注被映射到四个本体学:细胞系本体学 (CLO),细胞本体学 (CL),Uber-anatomy本体学 (UBERON) 和BRENDA组织本体学 (BTO).
- 模型输出与text2term工具的注释进行了比较,使用来自公共生物数据库的数据集.
主要成果:
- 一个精心调整的GPT模型在注释细胞系和细胞类型方面显著超过了基础模型和text2term.
- 微调的模型实现了高回忆率 (88-97%) 和中等精度 (47-64%) 的CL和UBERON本体.
- 基础的LLM在本体注释任务中表现明显较差.
结论:
- 精心调整的LLM可以加速和提高生物数据注释的准确性,帮助语义集成.
- 仍然存在一些挑战,包括在不同的本体学类别中具有可变精度.
- 专家策划仍然对于验证LLM生成的注释和确保数据完整性至关重要.
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