信息理论的性能衍生:用于差异诊断和聚类的语义相似性算法
Ben Coleman1, Daniel Danis2, Justin Reese3
1Computational Biology, The Jackson Laboratory for Genomic Medicine, Discovery Dr, CT 06032, USA.
bioRxiv : the preprint server for biology
|December 3, 2025
概括
使用人类现象型本体学 (HPO) 的语义相似性分析有助于比较临床表现. 这项研究比较了各种算法,发现Phenomizer对差异诊断和对队列分层的规范化方法有效.
科学领域:
- 计算生物学是一种计算生物学.
- 医疗信息学医学信息学
- 生物信息学是一种生物信息学.
背景情况:
- 使用人类现象型本体学 (HPO) 的语义相似性分析对于比较临床表现至关重要.
- 现有的方法如Phenomizer和基于集合的方法缺乏统一的数学模型和全面的比较.
- 这些方法对于差异诊断和队列分层至关重要.
研究的目的:
- 描述基于信息理论的语义相似性算法.
- 提出现有的语义相似性模型的新变体.
- 为了比较差异诊断和表型聚类的不同算法的性能.
主要方法:
- 描述了使用信息理论推导的语义相似性算法.
- 提出了三种新的语义相似性模型变化.
- 使用综合数据集对差异诊断排名和表型聚类进行算法性能比较.
主要成果:
- 当疾病仅根据相似性排名时,Phenomizer在差异诊断方面表现出卓越的性能,没有p值.
- 使用条件信息的非规范化算法在差异诊断中显示了与Phenomizer可比的性能.
- 规范化算法被证明是对队列分层和表型聚类最有效的.
结论:
- 语义相似性算法的选择会根据具体应用 (差异诊断与队列聚类) 影响性能.
- 现象化和非规范化的条件信息方法适用于差异诊断.
- 规范化算法对于表型聚类和队列分层是最佳的.
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