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miTAR: a hybrid deep learning-based approach for predicting miRNA targets

BACKGROUND: microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and re...

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Autores principales: Gu, Tongjun, Zhao, Xiwu, Barbazuk, William Bradley, Lee, Ji-Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7912887/
https://www.ncbi.nlm.nih.gov/pubmed/33639834
http://dx.doi.org/10.1186/s12859-021-04026-6
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author Gu, Tongjun
Zhao, Xiwu
Barbazuk, William Bradley
Lee, Ji-Hyun
author_facet Gu, Tongjun
Zhao, Xiwu
Barbazuk, William Bradley
Lee, Ji-Hyun
author_sort Gu, Tongjun
collection PubMed
description BACKGROUND: microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets. RESULTS: We developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets. CONCLUSIONS: We present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR. Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models.
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spelling pubmed-79128872021-03-02 miTAR: a hybrid deep learning-based approach for predicting miRNA targets Gu, Tongjun Zhao, Xiwu Barbazuk, William Bradley Lee, Ji-Hyun BMC Bioinformatics Methodology Article BACKGROUND: microRNAs (miRNAs) have been shown to play essential roles in a wide range of biological processes. Many computational methods have been developed to identify targets of miRNAs. However, the majority of these methods depend on pre-defined features that require considerable efforts and resources to compute and often prove suboptimal at predicting miRNA targets. RESULTS: We developed a novel hybrid deep learning-based (DL-based) approach that is capable of predicting miRNA targets at a higher accuracy. This approach integrates convolutional neural networks (CNNs) that excel in learning spatial features and recurrent neural networks (RNNs) that discern sequential features. Therefore, our approach has the advantages of learning both the intrinsic spatial and sequential features of miRNA:target. The inputs for our approach are raw sequences of miRNAs and genes that can be obtained effortlessly. We applied our approach on two human datasets from recently miRNA target prediction studies and trained two models. We demonstrated that the two models consistently outperform the previous methods according to evaluation metrics on test datasets. Comparing our approach with currently available alternatives on independent datasets shows that our approach delivers substantial improvements in performance. We also show with multiple evidences that our approach is more robust than other methods on small datasets. Our study is the first study to perform comparisons across multiple existing DL-based approaches on miRNA target prediction. Furthermore, we examined the contribution of a Max pooling layer in between the CNN and RNN and demonstrated that it improves the performance of all our models. Finally, a unified model was developed that is robust on fitting different input datasets. CONCLUSIONS: We present a new DL-based approach for predicting miRNA targets and demonstrate that our approach outperforms the current alternatives. We supplied an easy-to-use tool, miTAR, at https://github.com/tjgu/miTAR. Furthermore, our analysis results support that Max Pooling generally benefits the hybrid models and potentially prevents overfitting for hybrid models. BioMed Central 2021-02-27 /pmc/articles/PMC7912887/ /pubmed/33639834 http://dx.doi.org/10.1186/s12859-021-04026-6 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Methodology Article
Gu, Tongjun
Zhao, Xiwu
Barbazuk, William Bradley
Lee, Ji-Hyun
miTAR: a hybrid deep learning-based approach for predicting miRNA targets
title miTAR: a hybrid deep learning-based approach for predicting miRNA targets
title_full miTAR: a hybrid deep learning-based approach for predicting miRNA targets
title_fullStr miTAR: a hybrid deep learning-based approach for predicting miRNA targets
title_full_unstemmed miTAR: a hybrid deep learning-based approach for predicting miRNA targets
title_short miTAR: a hybrid deep learning-based approach for predicting miRNA targets
title_sort mitar: a hybrid deep learning-based approach for predicting mirna targets
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7912887/
https://www.ncbi.nlm.nih.gov/pubmed/33639834
http://dx.doi.org/10.1186/s12859-021-04026-6
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