Review

. 2020 Oct:88:107364.

doi: 10.1016/j.compbiolchem.2020.107364. Epub 2020 Aug 27.

ncRDeep: Non-coding RNA classification with convolutional neural network

Tuvshinbayar Chantsalnyam¹, Dae Yeong Lim², Hilal Tayara³, Kil To Chong⁴

Affiliations

¹ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
² Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea.
³ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea. Electronic address: hilaltayara@jbnu.ac.kr.
⁴ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea. Electronic address: kitchong@jbnu.ac.kr.

PMID: 32890916
DOI: 10.1016/j.compbiolchem.2020.107364

Review

ncRDeep: Non-coding RNA classification with convolutional neural network

Tuvshinbayar Chantsalnyam et al. Comput Biol Chem. 2020 Oct.

. 2020 Oct:88:107364.

doi: 10.1016/j.compbiolchem.2020.107364. Epub 2020 Aug 27.

Authors

Tuvshinbayar Chantsalnyam¹, Dae Yeong Lim², Hilal Tayara³, Kil To Chong⁴

Affiliations

¹ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
² Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea.
³ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea. Electronic address: hilaltayara@jbnu.ac.kr.
⁴ Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea. Electronic address: kitchong@jbnu.ac.kr.

PMID: 32890916
DOI: 10.1016/j.compbiolchem.2020.107364

Abstract

A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involved in many biological processes, diseases, and cancers. Numerous ncRNAs have been identified and classified with high throughput sequencing technology. Hence, accurate ncRNAs class prediction is important and necessary for further study of their functions. Several computation techniques have been employed to predict the class of ncRNAs. Recent classification methods used the secondary structure as their primary input. However, the computational tools of RNA secondary structure are not accurate enough which affects the final performance of ncRNAs predictors. In this paper, we propose a simple yet efficient method, called ncRDeep, for ncRNAs prediction. It uses a simple convolutional neural network and RNA sequence information only. The ncRDeep was evaluated on benchmark datasets and the comparison results showed that the ncRDeep outperforms the state-of-the-art methods significantly. More specifically, the average accuracy was improved by 8.32%. Finally, we built a freely accessible web server for the developed tool ncRDeep at http://home.jbnu.ac.kr/NSCL/ncRDeep.htm.

Keywords: Classification; Convolution neural network; Deep learning; Non-coding RNA.

PubMed Disclaimer

Cited by

Comparison and benchmark of deep learning methods for non-coding RNA classification.
Creux C, Zehraoui F, Radvanyi F, Tahi F. Creux C, et al. PLoS Comput Biol. 2024 Sep 12;20(9):e1012446. doi: 10.1371/journal.pcbi.1012446. eCollection 2024 Sep. PLoS Comput Biol. 2024. PMID: 39264986 Free PMC article.
BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification.
Avila Santos AP, de Almeida BLS, Bonidia RP, Stadler PF, Stefanic P, Mandic-Mulec I, Rocha U, Sanches DS, de Carvalho ACPLF. Avila Santos AP, et al. RNA Biol. 2024 Jan;21(1):1-12. doi: 10.1080/15476286.2024.2329451. Epub 2024 Mar 25. RNA Biol. 2024. PMID: 38528797 Free PMC article.
High-Accuracy ncRNA Function Prediction via Deep Learning Using Global and Local Sequence Information.
Orro A, Trombetti GA. Orro A, et al. Biomedicines. 2023 Jun 3;11(6):1631. doi: 10.3390/biomedicines11061631. Biomedicines. 2023. PMID: 37371726 Free PMC article.
MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding.
Dunkel H, Wehrmann H, Jensen LR, Kuss AW, Simm S. Dunkel H, et al. Int J Mol Sci. 2023 May 17;24(10):8884. doi: 10.3390/ijms24108884. Int J Mol Sci. 2023. PMID: 37240230 Free PMC article.
NCodR: A multi-class support vector machine classification to distinguish non-coding RNAs in Viridiplantae.
Nithin C, Mukherjee S, Basak J, Bahadur RP. Nithin C, et al. Quant Plant Biol. 2022 Oct 7;3:e23. doi: 10.1017/qpb.2022.18. eCollection 2022. Quant Plant Biol. 2022. PMID: 37077974 Free PMC article.

See all "Cited by" articles

Publication types

Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions

Substances

Actions

LinkOut - more resources

Full Text Sources
- Elsevier Science

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

ncRDeep: Non-coding RNA classification with convolutional neural network

Affiliations

ncRDeep: Non-coding RNA classification with convolutional neural network

Authors

Affiliations

Abstract

Similar articles

Cited by

Publication types

MeSH terms

Substances

LinkOut - more resources

Full Text Sources

Abstract

Similar articles

Cited by

Publication types

MeSH terms

Substances

Related information

LinkOut - more resources

Full Text Sources