Relational completion based non-negative matrix factorization for predicting metabolite-disease associations
Relational completion based non-negative matrix factorization for predicting metabolite-disease associations
复制标题
基于关系完成的非负矩阵分解用于预测代谢物与疾病的关联
DOI：
10.1016/j.knosys.2020.106238
复制
发表时间：
2020-09-27
影响因子：
8.8
通讯作者：
Fujita, Hamido
中科院分区：
文献类型：
--
作者：
Lei, Xiujuan;Tie, Jiaojiao;Fujita, Hamido
代谢物，也称为中间代谢物，是指在代谢过程中产生或消耗的物质。越来越多的证据表明代谢物在疾病研究中起着重要的作用。针对传统实验中发现代谢物与疾病之间的关联费时费力的问题，提出了一种计算方法RCNMF来预测代谢物与疾病之间的关联。首先计算疾病语义相似度和代谢产物的分子指纹相似度。代谢产物的分子指纹图谱相似性充分利用了代谢产物分子结构的内在信息。然后，我们修改了原始的代谢物-疾病关联矩阵，用0和1之间的数字替换了一些0值。最后，我们使用非负矩阵分解算法来预测潜在的代谢物-疾病关联。我们采用交叉验证机制来验证我们所提出的方法的性能。基于留一法和五重交叉验证法的AUC值分别达到0.9566和0.9430。最后，通过对常见疾病的案例分析，验证了该方法的有效性.因此，上级的实验结果表明，我们的方法可以有效地预测潜在的疾病代谢物协会。(C)2020 Elsevier B. V.保留所有权利。
Metabolite, also known as intermediate metabolite, refers to substances produced or consumed in the metabolic processes. There are growing evidences that metabolites play an important role in the study of diseases. Due to the traditional experiments, it is time-consuming and luxurious to find the associations between metabolite and disease, we proposed a computational method, called RCNMF, to predict metabolite-disease associations. Firstly, we calculate the disease semantic similarity and the molecular fingerprint similarity of metabolite. The molecular fingerprint similarity of metabolite makes full use of the molecular structure internal information of metabolites. Then, we modify the original metabolite-disease associations matrix to replace some values of 0 with numbers between 0 and 1. Finally, we use the non-negative matrix factorization algorithm to predict potential metabolite-disease associations. We adopt the cross-validation mechanism to verify the performance of our proposed method. The AUC values of based the Leave-one-out cross validation measurement and the Five-fold cross validation measurement reach 0.9566 and 0.9430, respectively. What is more, case studies of common diseases also illustrate the effectiveness of our method. Thus, the superior experimental results show that our method can effectively predict the potential disease-metabolites associations. (C) 2020 Elsevier B.V. All rights reserved.