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Phen2Disease:一种由表型驱动的疾病和基因优先级模型,通过双向最大匹配的语义相似性来确定疾病和基因优先级
Weiqi Zhai1, Xiaodi Huang2, Nan Shen3
1Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China.
Briefings in bioinformatics
|May 30, 2023
概括
通过分析患者和疾病表型,Phen2Disease改进了罕见疾病基因组诊断. 这种新的方法增强了疾病和基因优先级,优于现有的方法.
科学领域:
- 基因组学就是基因组学.
- 医疗信息学 医疗信息学
- 罕见疾病 罕见疾病
背景情况:
- 基于人类现象型本体学 (HPO) 的方法越来越多地用于罕见疾病的基因组诊断.
- 目前的HPO方法并不能充分利用现有的患者和疾病表型信息.
研究的目的:
- 为了引入Phen2Disease,一种用于在罕见疾病诊断中优先考虑疾病和基因的新方法.
- 改进表型数据在基因组分析中的利用.
主要方法:
- 利用患者和疾病表型集之间的双向最大匹配语义相似性.
- 雇佣的HPO知识库和表型信息用于分析.
- 在六个真实数据队列 (2051例) 和两个模拟队列 (1000例) 上验证.
主要成果:
- 与三种最先进的方法相比,Phen2Disease表现优越,特别是在具有较少HPO项的队列中.
- 患者更高的信息内容得分与更具体的数据和更高的预测准确性相关.
- 该方法通过排名的疾病和相关的患者HPO术语提供了高度的解释性.
结论:
- Phen2Disease使用表型数据为罕见疾病的基因组诊断提供了一种新且有效的方法.
- 该方法显示了在诊断罕见遗传疾病方面具有重大临床影响的潜力.
- Phen2Disease在GitHub上公开提供,用于更广泛的研究和临床应用.
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