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邻居拓意识知识图学习和微生物偏好推断药物-微生物协会预测

Jing Gu¹, Tiangang Zhang², Yihang Gao¹

¹School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China.

Journal of chemical information and modeling

|January 2, 2025

概括

新的PCMDA方法通过分析生物网络和实体偏好,准确地预测微生物与药物之间的关联. 这种计算方法增强了对微生物如何影响药物有效性的理解.

科学领域:

药物基因组学和计算生物学
微生物组研究和药物相互作用

背景情况:

人类微生物群通过代谢药物制剂,显著影响药物疗效.
预测微生物与药物关联的现有计算方法往往忽略了复杂的网络结构和实体特定的偏好.

研究的目的:

开发一种新的计算方法,PCMDA,用于准确预测微生物与药物之间的关联.
通过结合社区拓,协会偏好和多视角特征来解决以前方法的局限性.

主要方法:

构建一个全面的微生物疾病药物知识图.
使用随机步行与邻里重启和距离级别注意力生成拓嵌入.
通过知识图学习模块和多层感知器网络集成拓结构和关系语义.
纳入微生物药物偏好,使用信息级别的注意力和功能编码的双门网络.

主要成果:

与七种最先进的方法相比,PCMDA在预测微生物与药物关联方面表现优越.
案例研究证实PCMDA能够识别与特定药物相关的可靠候选微生物.

结论:

PCMDA有效地利用拓信息和实体特定偏好来准确地预测微生物与药物之间的关联.
开发的方法为了解宿主-微生物-药物相互作用和优化药物治疗提供了有价值的工具.

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