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GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder

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...

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Detalles Bibliográficos
Autores principales: Li, Lei, Wang, Yu-Tian, Ji, Cun-Mei, Zheng, Chun-Hou, Ni, Jian-Cheng, Su, Yan-Sen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8694430/
https://www.ncbi.nlm.nih.gov/pubmed/34890410
http://dx.doi.org/10.1371/journal.pcbi.1009655
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author Li, Lei
Wang, Yu-Tian
Ji, Cun-Mei
Zheng, Chun-Hou
Ni, Jian-Cheng
Su, Yan-Sen
author_facet Li, Lei
Wang, Yu-Tian
Ji, Cun-Mei
Zheng, Chun-Hou
Ni, Jian-Cheng
Su, Yan-Sen
author_sort Li, Lei
collection PubMed
description 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 convolutional autoencoder (GCAE) was 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 performances 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 was beneficial to infer potential associations of miRNA-disease.
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spelling pubmed-86944302021-12-23 GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder Li, Lei Wang, Yu-Tian Ji, Cun-Mei Zheng, Chun-Hou Ni, Jian-Cheng Su, Yan-Sen PLoS Comput Biol Research Article 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 convolutional autoencoder (GCAE) was 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 performances 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 was beneficial to infer potential associations of miRNA-disease. Public Library of Science 2021-12-10 /pmc/articles/PMC8694430/ /pubmed/34890410 http://dx.doi.org/10.1371/journal.pcbi.1009655 Text en © 2021 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Li, Lei
Wang, Yu-Tian
Ji, Cun-Mei
Zheng, Chun-Hou
Ni, Jian-Cheng
Su, Yan-Sen
GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
title GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
title_full GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
title_fullStr GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
title_full_unstemmed GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
title_short GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder
title_sort gcaemda: predicting mirna-disease associations via graph convolutional autoencoder
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8694430/
https://www.ncbi.nlm.nih.gov/pubmed/34890410
http://dx.doi.org/10.1371/journal.pcbi.1009655
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