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基于双分支图形的卷积网络预测潜在的微生物疾病关联

Jing Chen¹, Yongjun Zhu¹, Qun Yuan²

¹School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China.

Journal of cellular and molecular medicine

|August 1, 2024

概括

本研究介绍了DBGCNMDA,这是一种使用双图卷积网络识别微生物与疾病关联的计算框架. 它有效地预测了潜在的联系,帮助预防疾病和药物开发.

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.
基因组学和微生物组学

背景情况:

了解微生物与疾病的关联对于疾病预防,诊断,药物开发和个性化医学至关重要.
确认微生物与疾病关系的传统实验室方法耗时且昂贵.
需要先进的计算框架来有效地预测新的微生物疾病关联.

研究的目的:

开发和验证一种新的计算方法,DBGCNMDA,用于识别微生物与疾病的关联.
利用双图卷积网络 (GCNs) 来提高特征提取和预测准确性.
解决数据维度问题并优化网络连接,以实现强大的关联预测.

主要方法:

使用集成功能相似性和高斯关联频谱内核 (GAPK) 相似性计算了微生物和疾病相似性矩阵.
使用双分支GCN模块 (GlobalGCN和LocalGCN) 来从生物网络中提取语义信息.
优化网络连接与随机步行与重启 (RWR) 并使用相似度矩阵作为初始特征矩阵.

主要成果:

在五倍交叉验证 (5倍CV) 中,DBGCNMDA模型实现了高精度,ROC曲线下的面积 (AUC) 为0.9559,精度回忆曲线下的面积 (AUPR) 为0.9630.
使用已发表的实验数据进行的案例研究证实了大量预测的微生物与疾病的关联.
该方法通过扩展疾病节点,有效地解决了低数据维度问题,并结合了同源邻居信息.

结论:

DBGCNMDA是一种强大而有效的计算工具,用于预测潜在的微生物疾病关联.
双分支GCN方法提高了对复杂的生物网络关系的理解.
这些发现支持计算方法在加速发现微生物与疾病联系的临床应用中的实用性.

关键词:

关联预测预测预测疾病疾病疾病疾病双分支图表卷积网络的卷积网络.一个微生物的微生物.随机步行与重新启动.

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