GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

Abstract

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolution autoencoder (GCAE) is utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performance were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA were beneficial to infer potential associations of miRNA-disease.

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

“…Chen et al (2019b) integrated ensemble learning and dimensionality reduction based on principal component analysis for inferring potential miRNA-disease associations. Li et al (2021b) used a graph convolutional autoencoder to calculate association scores based on the two sub-networks of miRNAs and diseases in a heterogeneous network and adopted an average ensemble method to obtain the final prediction score. Li et al (2021c) proposed a novel graph autoencoder method named GAEMDA to predict the potential miRNA-disease associations in an endto-end manner.…”

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations

“…GCAEMDA [ 48 ]: GCAEMDA uses graph convolutional autoencoder to learn scores of miRNA-disease from miRNA-based and disease-based sub-networks, and adopts an average ensemble way to integrate two prediction scores for the final miRNA-disease association prediction.…”

Predicting miRNA-disease associations based on graph attention network with multi-source information

“…We obtain the dataset of HMDD v3.2 from Li’s model [ 44 ], which includes 4189 interactions between 437 miRNAs and 431 diseases, 8172 relationships between 861 lncRNAs and 437 miRNAs, and 4518 lncRNA-disease correlations. To obtain a systematic and convincing comparison, we compare GATMDA method with several baselines on HMDD v3.2, including LAGCN [ 39 ], NEMII [ 22 ] and GCAEMDA [ 45 ]. LAGCN employed attention mechanisms to fuse the features of multiple graph convolutional layers for drug-disease association prediction.…”