Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features

• Published in: Neural networks Vol. 165; pp. 491 - 505
• Main Authors: Guo, Yanbu, Zhou, Dongming, Ruan, Xiaoli, Cao, Jinde
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.08.2023
• Subjects: Disease-miRNA associations; Feature fusion; Graph autoencoders; Neural networks; Graph autoencoders; Feature fusion; Neural networks; Disease-miRNA associations
• ISSN: 0893-6080; 1879-2782; 1879-2782
• DOI: 10.1016/j.neunet.2023.05.052

MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better understand the development and diagnosis of complex human diseases via computational algorithms. The work presents a variational gated autoencoder-based feature extraction model to extract complex contextual features for inferring potential disease-miRNA associations. Specifically, our model fuses three different similarities of miRNAs into a comprehensive miRNA network and then combines two various similarities of diseases into a comprehensive disease network, respectively. Then, a novel graph autoencoder is designed to extract multilevel representations based on variational gate mechanisms from heterogeneous networks of miRNAs and diseases. Finally, a gate-based association predictor is devised to combine multiscale representations of miRNAs and diseases via a novel contrastive cross-entropy function, and then infer disease-miRNA associations. Experimental results indicate that our proposed model achieves remarkable association prediction performance, proving the efficacy of the variational gate mechanism and contrastive cross-entropy loss for inferring disease-miRNA associations.