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Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases

Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA–disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing comp...

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Autores principales: Xu, Long, Li, Xiaokun, Yang, Qiang, Tan, Long, Liu, Qingyuan, Liu, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9314862/
https://www.ncbi.nlm.nih.gov/pubmed/35903359
http://dx.doi.org/10.3389/fgene.2022.936823
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author Xu, Long
Li, Xiaokun
Yang, Qiang
Tan, Long
Liu, Qingyuan
Liu, Yong
author_facet Xu, Long
Li, Xiaokun
Yang, Qiang
Tan, Long
Liu, Qingyuan
Liu, Yong
author_sort Xu, Long
collection PubMed
description Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA–disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA). First, we constructed a microRNA similarity network, a disease similarity network, and Gaussian interaction profile kernel similarity based on the known miRNA–disease association and comprehensive similarity of miRNAs (diseases). Next, an integrated similarity feature network with the full underlying relationships of miRNA–disease pairwise was obtained. Then, the similarity feature network was fed into the BGANMDA model to learn advanced traits in latent space. Finally, we ranked an association score list and predicted the associations between miRNA and disease. In our experiment, a five-fold cross validation was applied to estimate BGANMDA’s performance, and an area under the curve (AUC) of 0.9319 and a standard deviation of 0.00021 were obtained. At the same time, in the global and local leave-one-out cross validation (LOOCV), the AUC value and standard deviation of BGANMDA were 0.9116 ± 0.0025 and 0.8928 ± 0.0022, respectively. Furthermore, BGANMDA was employed in three different case studies to validate its prediction capability and accuracy. The experimental results of the case studies showed that 46, 46, and 48 of the top 50 prediction lists had been identified in previous studies.
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spelling pubmed-93148622022-07-27 Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases Xu, Long Li, Xiaokun Yang, Qiang Tan, Long Liu, Qingyuan Liu, Yong Front Genet Genetics Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA–disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA). First, we constructed a microRNA similarity network, a disease similarity network, and Gaussian interaction profile kernel similarity based on the known miRNA–disease association and comprehensive similarity of miRNAs (diseases). Next, an integrated similarity feature network with the full underlying relationships of miRNA–disease pairwise was obtained. Then, the similarity feature network was fed into the BGANMDA model to learn advanced traits in latent space. Finally, we ranked an association score list and predicted the associations between miRNA and disease. In our experiment, a five-fold cross validation was applied to estimate BGANMDA’s performance, and an area under the curve (AUC) of 0.9319 and a standard deviation of 0.00021 were obtained. At the same time, in the global and local leave-one-out cross validation (LOOCV), the AUC value and standard deviation of BGANMDA were 0.9116 ± 0.0025 and 0.8928 ± 0.0022, respectively. Furthermore, BGANMDA was employed in three different case studies to validate its prediction capability and accuracy. The experimental results of the case studies showed that 46, 46, and 48 of the top 50 prediction lists had been identified in previous studies. Frontiers Media S.A. 2022-07-12 /pmc/articles/PMC9314862/ /pubmed/35903359 http://dx.doi.org/10.3389/fgene.2022.936823 Text en Copyright © 2022 Xu, Li, Yang, Tan, Liu and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Xu, Long
Li, Xiaokun
Yang, Qiang
Tan, Long
Liu, Qingyuan
Liu, Yong
Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases
title Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases
title_full Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases
title_fullStr Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases
title_full_unstemmed Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases
title_short Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases
title_sort application of bidirectional generative adversarial networks to predict potential mirnas associated with diseases
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9314862/
https://www.ncbi.nlm.nih.gov/pubmed/35903359
http://dx.doi.org/10.3389/fgene.2022.936823
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