对抗性规范化自编码器图形神经网络用于微生物疾病协会预测预测.
Limuxuan He1, Quan Zou1,2, Qi Dai3
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Qingshuihe Campus, 2006 Xiyuan Avenue, West District, High-tech Zone, Chengdu, Sichuan 610054, China.
Briefings in bioinformatics
|November 11, 2024
概括
预测微生物与疾病的关联对于了解疾病至关重要. 我们的新型深度学习模型SARMDA有效地使用生物网络识别这些链接,提高预测准确度.
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.
- 机器学习在医疗保健中的应用
背景情况:
- 微生物与众多人类疾病有关,需要准确的关联预测.
- 识别微生物与疾病联系的实验方法昂贵且耗时.
- 深度学习和生物网络为大规模关联预测提供了一个有希望的方法.
研究的目的:
- 开发一种高效的计算方法来预测微生物与疾病的关联.
- 利用图形神经网络和自动编码器来提高预测准确度.
- 为了确定新型微生物对人类疾病的贡献.
主要方法:
- 拟议的微生物疾病协会预测 (SARMDA) 的堆叠对抗规范化,一个对抗规范化的自编码器图形神经网络算法.
- 构建了一个整合微生物和疾病拓和功能相似性的异质网络.
- 采用基于GraphSAGE的自编码器和对抗性规范化的自编码器图形神经网络嵌入模型.
主要成果:
- 在人类微生物疾病协会数据库 (HMDAD) 中,SARMDA获得了高性能,AUC为0.9891和AUPR为0.9902.
- 在Disbiome数据集中,SARMDA的AUC为0.9328和AUPR为0.9233,超过了其他八种方法.
- 通过对喘和炎症性肠病的案例研究验证的模型有效性.
结论:
- 与现有的方法相比,SARMDA在预测微生物疾病关联方面表现出卓越的表现.
- 该模型集成网络拓和属性的能力提高了预测.
- 这种方法有助于更深入地了解微生物与疾病的关系以及潜在的治疗点.
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