Predicting MiRNA-disease associations by multiple meta-paths fusion graph embedding model

Abstract

Background Many studies prove that miRNAs have significant roles in diagnosing and treating complex human diseases. However, conventional biological experiments are too costly and time-consuming to identify unconfirmed miRNA-disease associations. Thus, computational models predicting unidentified miRNA-disease pairs in an efficient way are becoming promising research topics. Although existing methods have performed well to reveal unidentified miRNA-disease associations, more work is still needed to improve prediction performance. Results In this work, we present a novel multiple meta-paths fusion graph embedding model to predict unidentified miRNA-disease associations (M2GMDA). Our method takes full advantage of the complex structure and rich semantic information of miRNA-disease interactions in a self-learning way. First, a miRNA-disease heterogeneous network was derived from verified miRNA-disease pairs, miRNA similarity and disease similarity. All meta-path instances connecting miRNAs with diseases were extracted to describe intrinsic information about miRNA-disease interactions. Then, we developed a graph embedding model to predict miRNA-disease associations. The model is composed of linear transformations of miRNAs and diseases, the means encoder of a single meta-path instance, the attention-aware encoder of meta-path type and attention-aware multiple meta-path fusion. We innovatively integrated meta-path instances, meta-path based neighbours, intermediate nodes in meta-paths and more information to strengthen the prediction in our model. In particular, distinct contributions of different meta-path instances and meta-path types were combined with attention mechanisms. The data sets and source code that support the findings of this study are available at https://github.com/dangdangzhang/M2GMDA. Conclusions M2GMDA achieved AUCs of 0.9323 and 0.9182 in global leave-one-out cross validation and fivefold cross validation with HDMM V2.0. The results showed that our method outperforms other prediction methods. Three kinds of case studies with lung neoplasms, breast neoplasms, prostate neoplasms, pancreatic neoplasms, lymphoma and colorectal neoplasms demonstrated that 47, 50, 49, 48, 50 and 50 out of the top 50 candidate miRNAs predicted by M2GMDA were validated by biological experiments. Therefore, it further confirms the prediction performance of our method.

Computational Method Using Heterogeneous Graph Convolutional Network Model Combined With Reinforcement Layer For MiRNA-Disease Association Prediction

“…Fig. 6 HGCNELMDA model (1) Step 1: Build miRNA-disease isomerization map according to literature [41].Through integrated disease semantic similarity network , The known miRNA-disease association matrix is the same and an integratedmiRNA functional similarity network constructed into a miRNA-disease heterogeneous map , as shown in Formula (1) :…”

“…extraction methods of graphs are spatial domain and Spectral domain. According to the explanation in Literature [41], the spatial method means that the neighbor nodes connected with the vertex are directly used to extract features. But the spectral method hopes to realize the convolution operation on the graph with the help of the graph theory, and studies the properties of the graph with the eigenvalues and eigenvectors of the Laplace matrix of the graph.…”

A miRNA-Disease Association Identification Method Based on Reliable Negative Sample Selection and Improved Single-Hidden Layer Feedforward Neural Network

“…In addition, Chen et al [31] presented a regularized least squares method (RLSMDA) without negative samples to reconstruct the missing associations for all diseases. Zhang et al [32] adopted a multiple meta-paths fusion graph embedding approach to infer unidentified miRNA-disease associations.…”

BioNE: Integration of network embeddings for supervised learning

“…(2) protein-related applications, such as protein-protein interactions (PPIs) (21)(22)(23)(24) and protein/gene disease interactions (25)(26)(27)(28)(29)(30)(31); and (3) transcriptomics-related applications, such as lncRNAs-diseases associations (32)(33)(34)(35) and miRNAdisease associations (36)(37)(38)(39)(40)(41)(42)(43) and many other applications (44)(45)(46)(47)(48)(49)(50). Since network embedding methods were not originally developed for biological networks, their performance in obtaining different biological network features is yet to be established.…”