Table_3_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（miRNA）是一类对人类疾病相关的多种生物过程产生深远影响的微小非编码RNA。推断潜在miRNA-疾病关联对于人类疾病的研究具有重要意义，如疾病预防、疾病诊断和药物开发。在本研究中，我们提出了一种新颖的异构网络嵌入方法，称为基于模块的动态邻域非负矩阵三角分解（MDN-NMTF），用于预测miRNA-疾病关联。MDN-NMTF构建了一个包含疾病相似性网络、miRNA相似性网络以及已知miRNA-疾病关联网络的异构网络。随后，它在异构网络中学习miRNA和疾病的潜在向量表示。最后，通过潜在miRNA和疾病向量的乘积计算关联概率。MDN-NMTF不仅成功整合了miRNA和疾病的多样生物信息以预测miRNA-疾病关联，而且在学习向量表示的过程中考虑了miRNA和疾病的模块属性，从而最大限度地保留了异构网络的结构信息和网络属性。同时，我们通过利用模块信息，将MDN-NMTF扩展至新版本（称为MDN-NMTF2），以提升miRNA-疾病关联预测能力。我们的方法与其他四种现有方法一起应用于预测四个数据库中的miRNA-疾病关联。预测结果表明，与四种现有方法相比，我们的方法能够将miRNA-疾病关联预测提升至更高水平。