通过深度学习算法识别和验证与烧死相关的签名,用于通过深度学习算法识别活性结核病
Yuchen Liu1,2,3,4, Lifan Zhang1,2,3, Fengying Wu1,2,3
1Division of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
这项研究确定了用于诊断活跃结核病 (ATB) 的与热致死相关的签名. 使用AIM2,CASP8和NAIP的新型诊断模型显示高准确度,有助于ATB控制.
科学领域:
- 免疫学 免疫学 免疫学
- 基因组学就是基因组学.
- 传染性疾病 传染性疾病
背景情况:
- 由Mycobacterium tuberculosis (M.tb) 引起的活跃结核病 (ATB) 是一个主要的全球健康威胁.
- 潜伏结核病感染 (LTBI) 很常见,但难以诊断,阻碍了根除的努力.
研究的目的:
- 为了确定与活性结核病 (ATB) 诊断的灭相关的分子特征.
- 根据这些签名开发和验证基于ATB的诊断模型.
主要方法:
- 在 GEO 数据集 (GSE39940,GSE37250) 上进行权重基因同表达网络分析 (WGCNA).
- 使用神经网络算法识别与烧灭相关的模块和签名.
- 使用单独的队列验证诊断模型的验证.
主要成果:
- WGCNA确定了九个共同表达模块,其中模块1与热症有很强的相关性.
- 通过使用AIM2,CASP8和NAIP.开发了一个与火溶性相关的签名 (PRS) 模型.
- 该PRS模型显示了高的诊断准确性 (AUC为0.946和0.787在两个队列).
结论:
- 与热相关的特征与ATB.ATB的病变发生有关.
- 一个新的PRS诊断模型显示了准确和高效的ATB诊断的巨大潜力.
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