变异贝叶斯多核自适应深度融合用于微生物相关药物预测
Yingjun Ma1, MingXu Luo2, Liyu Yan1
1School of Mathematics and Statistics, Xiamen University of Technology, Xiamen 361024, China.
Journal of chemical information and modeling
|February 2, 2026
概括
这项研究引入了一种新的计算模型,即变量贝叶斯多核自适应深度融合 (VBMKADF),用于预测微生物药物协会 (MDA). VBMKADF提供了一种比传统实验方法更有效,更准确的方法来发现潜在的候选药物,并了解微生物的作用.
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:
- 发现微生物药物协会 (MDA) 对药物发现和了解微生物机制至关重要.
- 用于MDA识别的实验方法耗时且昂贵,需要计算方法.
研究的目的:
- 开发一种有效的计算模型,用于预测新的微生物药物协会 (MDA).
- 与现有方法相比,提高MDA预测的准确性和效率.
主要方法:
- 提出了一个变化的贝叶斯多核自适应深度融合 (VBMKADF) 模型.
- 集成的多组学数据用于构建药物分子图和微生物超图.
- 采用多层图形和超图形卷曲,具有类似性融合的注意力机制,集成到贝叶斯逻辑矩阵因子化框架中.
- 利用一个可变的预期-最大化算法进行自适应推断和模型训练.
主要成果:
- 在预测微生物与药物关联方面,VBMKADF表现优于最先进的方法.
- 在平衡和不平衡数据集中获得更高的AUPR,AUC和F1分数.
- 案例研究验证了该模型作为MDA预测工具的有效性.
结论:
- VBMKADF模型提供了一种强大而准确的计算工具,用于预测微生物与药物之间的关联.
- 这种方法加速了药物发现,并加深了对微生物功能的理解.
- 与MDA识别的传统实验方法相比,VBMKADF提供了显著的进步.
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