基于Lnet变压器层和动态权重机制的综合多omics中风复发预测模型
Rui Miao1, Siyuan Li1, Daying Fan2
1Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, China.
Computers in biology and medicine
|July 11, 2024
概括
开发了一个新的多omics预测模型 (MPSR) 显著改善了中风复发预测. 这种模型提供了更高的准确性,并帮助临床医生识别患有复发性中风风险的患者.
科学领域:
- 神经学 神经学
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
背景情况:
- 脑卒中复发是一个重大的威胁,增加死亡率和残疾.
- 现有研究面临的挑战是多学科数据集成和MRI噪声降低.
- 缺乏可靠的多omics数据集来预测中风复发.
研究的目的:
- 开发一个高性能的多omics预测模型,用于中风复发.
- 解决MRI特征提取方面的挑战,并整合多种omics数据.
- 为了创建一个强大的数据集来预测中风复发.
主要方法:
- 从737名中风患者的MRI和临床数据编译成PSTSZC数据集.
- 引入了中风复发的综合多奥米克预测模型 (MPSR),结合了ResNet,Lnet转换器,LSTM和动态加权的DNN.
- 开发了一种用于MRI降噪的新型Lnet规范化层,以及用于omics数据集成的动态权重机制.
主要成果:
- 该MPSR模型的表现优于七个单一omics和六个最先进的多omics模型.
- MPSR实现了卓越的性能,精度,AUROC,特异性和灵敏度分别为0.96,0.97,1和0.94.
- 该模型有效地减少了来自MRI数据的噪声,并集成了多omics信息.
结论:
- MPSR是首个针对中风复发的高性能多omics预测模型.
- 该模型展示了作为诊断中风复发倾向的临床工具的潜力.
- 这项工作为预测疾病复发的多omics数据分析提供了强大的框架.
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