Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization.
Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization.
复制标题
利用图正则化非负矩阵分解进行人类微生物-疾病关联预测
DOI：
10.3389/fmicb.2018.02560
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发表时间：
2018
影响因子：
5.2
通讯作者：
Li Z
中科院分区：
文献类型：
--
作者：
He BS;Peng LH;Li Z
微生物是一种微观生物，可以以单细胞形式或细胞集落形式存在。近年来，越来越多的研究人员致力于揭示微生物与疾病的关系，因为微生物与许多复杂的人类疾病的预防，诊断和治疗密切相关。作为传统实验的有效补充，越来越多的基于各种算法的计算模型被提出用于微生物-疾病关联预测，以提高效率和节约成本。在这项工作中，我们开发了一种新的预测模型，图正则化非负矩阵因子分解人类微生物-疾病关联预测（GRNMFHMDA）。首先，微生物和疾病的相似性构建的基础上，基于高斯相互作用轮廓核相似性的微生物和疾病的相似性。随后，值得注意的是，我们利用了预处理步骤，其中未知的微生物-疾病对被分配了相关的似然分数，以避免对预测性能可能产生的负面影响。最后，我们实现了一个图正则化非负矩阵分解框架，以同时识别所有疾病的潜在关联。为了评估我们的模型的性能，交叉验证，包括全球留一交叉验证（LOOCV）和本地LOOCV实施。AUC为0.8715（全局LOOCV）和0.7898（局部LOOCV），证明了我们的计算模型的可靠性能。此外，我们对三种不同的人类疾病进行了两种类型的案例研究，以进一步分析GRNMFHMDA的预测性能，其中大多数预测的前10种疾病相关微生物被数据库HMDAD或实验文献验证。
A microbe is a microscopic organism which may exists in its single-celled form or in a colony of cells. In recent years, accumulating researchers have been engaged in the field of uncovering microbe-disease associations since microbes are found to be closely related to the prevention, diagnosis, and treatment of many complex human diseases. As an effective supplement to the traditional experiment, more and more computational models based on various algorithms have been proposed for microbe-disease association prediction to improve efficiency and cost savings. In this work, we developed a novel predictive model of Graph Regularized Non-negative Matrix Factorization for Human Microbe-Disease Association prediction (GRNMFHMDA). Initially, microbe similarity and disease similarity were constructed on the basis of the symptom-based disease similarity and Gaussian interaction profile kernel similarity for microbes and diseases. Subsequently, it is worth noting that we utilized a preprocessing step in which unknown microbe-disease pairs were assigned associated likelihood scores to avoid the possible negative impact on the prediction performance. Finally, we implemented a graph regularized non-negative matrix factorization framework to identify potential associations for all diseases simultaneously. To assess the performance of our model, cross validations including global leave-one-out cross validation (LOOCV) and local LOOCV were implemented. The AUCs of 0.8715 (global LOOCV) and 0.7898 (local LOOCV) proved the reliable performance of our computational model. In addition, we carried out two types of case studies on three different human diseases to further analyze the prediction performance of GRNMFHMDA, in which most of the top 10 predicted disease-related microbes were verified by database HMDAD or experimental literatures.
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