Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features
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-bas...
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| Published in | Neural networks Vol. 165; pp. 491 - 505 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.08.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0893-6080 1879-2782 1879-2782 |
| DOI | 10.1016/j.neunet.2023.05.052 |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Cao, Jinde Ruan, Xiaoli Guo, Yanbu Zhou, Dongming |
| Author_xml | – sequence: 1 givenname: Yanbu orcidid: 0000-0001-9532-2309 surname: Guo fullname: Guo, Yanbu email: guoyanbu@gmail.com organization: College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China – sequence: 2 givenname: Dongming orcidid: 0000-0003-0139-9415 surname: Zhou fullname: Zhou, Dongming email: zhoudm@ynu.edu.cn organization: School of Information Science and Engineering, Yunnan University, Kunming 650500, China – sequence: 3 givenname: Xiaoli orcidid: 0000-0002-4672-531X surname: Ruan fullname: Ruan, Xiaoli email: xlruan@gzu.edu.cn organization: State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China – sequence: 4 givenname: Jinde orcidid: 0000-0003-3133-7119 surname: Cao fullname: Cao, Jinde email: jdcao@seu.edu.cn organization: School of Mathematics, Southeast University, Nanjing 211189, China |
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| Keywords | Graph autoencoders Feature fusion Neural networks Disease-miRNA associations |
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| Snippet | MicroRNAs (miRNA) play critical roles in diverse biological processes of diseases. Inferring potential disease-miRNA associations enable us to better... |
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| SubjectTerms | Disease-miRNA associations Feature fusion Graph autoencoders Neural networks |
| Title | Variational gated autoencoder-based feature extraction model for inferring disease-miRNA associations based on multiview features |
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