多样本体学嵌入方法对与人类一致的多样本体学代表性进行基因疾病关联预测预测
Yihao Wang1,2, Philipp Wegner2,3, Daniel Domingo-Fernández1
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, 53757, Germany.
Heliyon
|November 29, 2023
概括
将生物医学本体学注入自然语言处理 (NLP) 模型中可以提高基因疾病关联预测. 这种以知识为导向的方法可以在没有复杂的培训的情况下提高模型性能,为生物医学研究提供了一个有前途的途径.
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:
- 生物医学知识图和本体学提供丰富的上下文信息.
- 利用这种结构化知识可以增强自然语言处理 (NLP) 任务.
- 基因疾病关联预测是生物医学研究中的一个关键挑战.
研究的目的:
- 调查整合对齐的生物医学本体学的影响作为先前知识纳入NLP模型.
- 评估知识注入模型对基因疾病关联预测的性能.
- 为了比较将上下文化知识与BERT等基线模型相结合的有效性.
主要方法:
- 从两个对齐的本体学中注入嵌入到NLP模型中.
- 在大型基因疾病关联数据集上评估模型性能.
- 将知识注入模型与缺乏上下文化知识的标准BERT模型进行了比较.
主要成果:
- 知识注入模型显示,与BERT相比,性能略有改善.
- 整合本体学之间的交叉引用增强了基本模型的性能.
- 最先进的知识图嵌入方法在联合图表上产生了有希望的结果.
结论:
- 语言模型从利用本体学的结构化知识中受益.
- 整合本体学可以提高NLP模型对生物医学任务的性能.
- 本研究的注释阶段的设计具有合理的复杂性.
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