基于修改图的注意力变化自编码器和随机森林的微生物药物协会的预测
Bo Wang1,2, Fangjian Ma1, Xiaoxin Du1
1College of Computer and Control Engineering, Qiqihar University, Qiqihar, China.
Frontiers in microbiology
|June 17, 2024
概括
这项研究引入了一种新的计算框架,MGAVAEMDA,用于识别微生物与药物之间的关联. 该模型整合了序列,结构和网络数据,在预测准确度方面表现优于现有方法.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 传统的微生物与药物相关性查是低效的.
- 现有的计算方法往往忽略了重要的生物数据类型.
研究的目的:
- 开发一个先进的计算框架来推断微生物与药物之间的关联.
- 整合多样化的生物信息,以提高预测准确度.
主要方法:
- 开发了一个修改的图表注意力变化自编码器 (MGAVAEMDA).
- 结合微生物序列,药物结构和网络拓.
- 使用变化自编码器和图表注意力来提取特征.
- 使用随机森林分类器进行协会评分.
主要成果:
- 与五种最先进的方法相比,MGAVAEMDA表现出优越的预测性能.
- 获得的AUC为0.9357和AUPR为0.9378,相对改善为1.76%和1.47%.
结论:
- 开发的框架准确地推断出潜在的微生物药物联系.
- 案例研究证实了MGAVAEMDA的可靠性,超过85%的预测得到了验证.
- MGAVAEMDA为药物研发提供了一种高效可靠的替代方案.
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