Multi-view Multichannel Attention Graph Convolutional Network for miRNA–disease association prediction

Abstract

Motivation: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data. Results: In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA–disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction by utilizing multichannel attention, which adaptively learns the importance of different features. Empirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.

A benchmark study of deep learning-based multi-omics data fusion methods for cancer

“…DL-based multi-omics data fusion methods aim to learn low-dimensional embeddings from the fusion of multi-omics data for various downstream tasks. According to our investigation, various DL-based data fusion methods can achieve this goal, including fully connected neural network (FCNN) [16][17][18][19][20], convolutional neural network (CNN) [21][22][23], autoencoder (AE) [24][25][26][27][28][29][30][31][32][33][34], graph neural network (GNN) [35][36][37][38][39], capsule network (CapsNet) [40,41], generative adversarial network (GAN) [42], and mixture DL-based models for multi-omics data fusion [43,44]. Most of these models in previous publications were used with different strategies (early or late fusion).…”

Predicting Multiple Types of Associations Between miRNAs and Diseases Based on Graph Regularized Weighted Tensor Decomposition

“…Li et al (2021c) proposed a novel graph autoencoder method named GAEMDA to predict the potential miRNA-disease associations in an endto-end manner. Tang et al (2021) utilized a graph convolutional network and attention mechanism to extract and enhance the latent representations of miRNA and disease in multiple views for reconstructing the miRNA-disease association matrix. Yan et al (2022) developed a new end-to-end deep learning method named PDMDA, which utilizes a fully connected network and graph neural network to extract the feature representations of miRNAs and diseases for deep-level miRNA-disease association prediction.…”

Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

“…Ji et al [ 37 ] proposed an autoencoder for detecting miRNA-disease associations. Tang et al [ 38 ] proposed a multi-view multi-channel graph attention networks to identify potential miRNA-disease associations. Graph Neural Networks (GNN) [ 39 ] have been proposed in deep learning on graphs.…”