Table_1_Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding.DOCX

MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In this work, we propose a novel heterogeneous network embedding-based method called MDN-NMTF (Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization) for predicting miRNA-disease associations. MDN-NMTF constructs a heterogeneous network of disease similarity network, miRNA similarity network and a known miRNA-disease association network. After that, it learns the latent vector representation for miRNAs and diseases in the heterogeneous network. Finally, the association probability is computed by the product of the latent miRNA and disease vectors. MDN-NMTF not only successfully integrates diverse biological information of miRNAs and diseases to predict miRNA-disease associations, but also considers the module properties of miRNAs and diseases in the course of learning vector representation, which can maximally preserve the heterogeneous network structural information and the network properties. At the same time, we also extend MDN-NMTF to a new version (called MDN-NMTF2) by using modular information to improve the miRNA-disease association prediction ability. Our methods and the other four existing methods are applied to predict miRNA-disease associations in four databases. The prediction results show that our methods can improve the miRNA-disease association prediction to a high level compared with the four existing methods.

微小RNA（MicroRNAs，miRNAs）是一类小型非编码RNA，对与人类疾病相关的多种生物学过程具有深远影响。挖掘潜在的miRNA-疾病关联有助于人类疾病的研究，例如疾病预防、疾病诊断与药物开发。本研究提出一种基于异质网络嵌入的新型预测方法，命名为MDN-NMTF（基于模块的动态邻域非负矩阵三分解，Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization），用于预测miRNA-疾病关联。MDN-NMTF首先构建融合疾病相似性网络、miRNA相似性网络与已知miRNA-疾病关联网络的异质网络。随后，该方法在该异质网络中学习miRNA与疾病的潜在向量表示。最终通过miRNA与疾病的潜在向量的乘积计算二者的关联概率。MDN-NMTF不仅能够有效整合miRNA与疾病的多维度生物学信息以预测其关联，还在向量表示学习过程中纳入了miRNA与疾病的模块属性，可最大程度保留异质网络的结构信息与网络特性。同时，本研究还利用模块信息对MDN-NMTF进行扩展，得到其升级版MDN-NMTF2，以提升miRNA-疾病关联预测性能。本研究将所提方法与另外四种现有方法应用于四个数据库中的miRNA-疾病关联预测任务。预测结果表明，相较于四种现有方法，本研究所提方法可将miRNA-疾病关联预测性能提升至更高水平。