NMGMDA:基于最小化矩阵核规范和图表注意力网络预测潜在的微生物药物关联的计算模型
Mingmin Liang1, Xianzhi Liu1, Qijia Chen2
1School of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, 410000, China.
Scientific reports
|January 5, 2024
概括
这项研究介绍了NMGMDA,一种新的计算模型,用于使用图表注意力网络和核规范最小化来预测微生物与药物之间的关联. NMGMDA表现出高精度,有助于药物发现和开发.
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.
- 药物发现 药物发现
背景情况:
- 预测微生物与药物之间的关联对于制药研究至关重要.
- 深度学习方法已经推进了生物医学预测.
- 现有的方法需要改进,以提高准确性和效率.
研究的目的:
- 开发一种新的计算模型,NMGMDA,用于推断潜在的微生物与药物相关性.
- 为了提高微生物与药物相互作用的预测准确度.
- 为药物重用和开发提供一个有价值的工具.
主要方法:
- 通过整合药物/微生物相似性和已知的关联,构建了一个异构的微生物药物网络.
- 雇员图表注意网络 (GAT) 和核规范最小化 (NNM) 用于得分计算.
- 使用五重交叉验证框架进行模型评估.
主要成果:
- 在MDAD和aBioflm数据库中,NMGMDA实现了0.9946的高曲线下的面积 (AUC).
- 在预测微生物与药物关联方面表现优于现有最先进的方法.
- 案例研究验证了对诸如西普罗素和莫西福素之类的药物以及HIV-1和Mycobacterium tuberculosis之类的微生物的预测.
结论:
- NMGMDA显示了准确的微生物药物协会预测的巨大潜力.
- 该模型的性能表明,在识别新型治疗策略方面具有实用性.
- 实验验证证证实了该模型在药物和微生物发现方面的有效性.
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