Multi-Kernel Graph Attention Deep Autoencoder for MiRNA-Disease Association Prediction

Accumulating evidence indicates that microRNAs (miRNAs) can control and coordinate various biological processes. Consequently, abnormal expressions of miRNAs have been linked to various complex diseases. Recognizable proof of miRNA-disease associations (MDAs) will contribute to the diagnosis and treatment of human diseases. Nevertheless, traditional experimental verification of MDAs is laborious and limited to small-scale. Therefore, it is necessary to develop reliable and effective computational methods to predict novel MDAs. In this work, a multi-kernel graph attention deep autoencoder (MGADAE) method is proposed to predict potential MDAs. In detail, MGADAE first employs the multiple kernel learning (MKL) algorithm to construct an integrated miRNA similarity and disease similarity, providing more biological information for further feature learning. Second, MGADAE combines the known MDAs, disease similarity, and miRNA similarity into a heterogeneous network, then learns the representations of miRNAs and diseases through graph convolution operation. After that, an attention mechanism is introduced into MGADAE to integrate the representations from multiple graph convolutional network (GCN) layers. Lastly, the integrated representations of miRNAs and diseases are input into the bilinear decoder to obtain the final predicted association scores. Corresponding experiments prove that the proposed method outperforms existing advanced approaches in MDA prediction. Furthermore, case studies related to two human cancers provide further confirmation of the reliability of MGADAE in practice.

Authors

• Cui-Na Jiao
• Feng Zhou Center for Applied Statistics and School of Statistics, Renmin University of China
• Bao-Min Liu
• Chun-Hou Zheng
• Jin-Xing Liu
• Ying-Lian Gao

Keywords

• Diseases
• Kernel
• Predictive models
• Deep learning
• Reliability
• Heterogeneous networks
• Bioinformatics
• Deep Autoencoder
• Graph Attention
• miRNA-disease Associations
• miRNA-disease Association Prediction
• Human Diseases
• Learning Algorithms
• Feature Learning
• Attention Mechanism
• Biological Information
• Heterogeneous Network
• Graph Convolutional Network
• Graph Convolution
• Multiple Kernel
• Representative Disease
• miRNA Disease
• Multiple Kernel Learning
• Breast Cancer
• Gastric Cancer
• Kernel Similarity
• Final Representation
• Positive Samples
• Number Of Kernels
• Negative Samples
• Trainable Weight Matrix
• Association Matrix
• Feature Representation
• Directed Acyclic Graph
• Graph-structured Data
• graph convolution neural network
• Humans
• MicroRNAs
• Reproducibility of Results
• Computational Biology
• Neoplasms
• Algorithms