利用双变异自编码器来解码微生物在疾病中的作用,用于传统医学发现
1School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.
Frontiers in pharmacology
|June 16, 2025
概括
这项研究引入了一种新的AI方法DVAMDA,用于预测传统医学中的微生物疾病关联 (MDA). 该模型增强了对微生物疾病联系的理解,并支持新药的开发.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 医学中的人工智能
背景情况:
- 传统医学提供了宝贵的知识,但在制剂成分和疗效数据方面面临着挑战.
- 人工智能 (AI) 提供了推动传统医学研究的机会,特别是在预测微生物疾病关联 (MDA) 方面.
研究的目的:
- 提出和评估一种基于人工智能的新方法DVAMDA,用于预测MDA.
- 为了提高MDA预测,利用多信息融合和深度学习技术.
主要方法:
- 使用了Graph SAGE编码器来提取节点结构信息.
- 采用双变量自编码器来提取潜在的概率分布和特定图形的属性.
- 集成的多信息融合,哈达马德产品和深度神经网络用于MDA预测.
主要成果:
- 在HMDAD和Disbiome数据集上,DVAMDA模型在多个评估指标上表现出色.
- 该方法有效地融合了各种信息来源,以实现强大的特征表示.
结论:
- 该DVAMDA模型显著提高了对微生物与疾病关系的理解.
- 这项研究为基于MDAs的传统医学药物开发提供了坚实的基础.
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