A hybrid CNN-LSTM model for pre-miRNA classification

Abstract miRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical pr...

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Autores principales:	Abdulkadir Tasdelen, Baha Sen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/812d4daa3902459488b7df570c9d745c
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id	oai:doaj.org-article:812d4daa3902459488b7df570c9d745c
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spelling	oai:doaj.org-article:812d4daa3902459488b7df570c9d745c2021-12-02T15:39:41ZA hybrid CNN-LSTM model for pre-miRNA classification10.1038/s41598-021-93656-02045-2322https://doaj.org/article/812d4daa3902459488b7df570c9d745c2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93656-0https://doaj.org/toc/2045-2322Abstract miRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.Abdulkadir TasdelenBaha SenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Abdulkadir Tasdelen Baha Sen A hybrid CNN-LSTM model for pre-miRNA classification
description	Abstract miRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.
format	article
author	Abdulkadir Tasdelen Baha Sen
author_facet	Abdulkadir Tasdelen Baha Sen
author_sort	Abdulkadir Tasdelen
title	A hybrid CNN-LSTM model for pre-miRNA classification
title_short	A hybrid CNN-LSTM model for pre-miRNA classification
title_full	A hybrid CNN-LSTM model for pre-miRNA classification
title_fullStr	A hybrid CNN-LSTM model for pre-miRNA classification
title_full_unstemmed	A hybrid CNN-LSTM model for pre-miRNA classification
title_sort	hybrid cnn-lstm model for pre-mirna classification
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/812d4daa3902459488b7df570c9d745c
work_keys_str_mv	AT abdulkadirtasdelen ahybridcnnlstmmodelforpremirnaclassification AT bahasen ahybridcnnlstmmodelforpremirnaclassification AT abdulkadirtasdelen hybridcnnlstmmodelforpremirnaclassification AT bahasen hybridcnnlstmmodelforpremirnaclassification
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A hybrid CNN-LSTM model for pre-miRNA classification

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