Literature DB >> 34242708

ncRDense: A novel computational approach for classification of non-coding RNA family by deep learning.

Tuvshinbayar Chantsalnyam1, Arslan Siraj1, Hilal Tayara2, Kil To Chong3.   

Abstract

With the rapidly growing importance of biological research, non-coding RNAs (ncRNA) attract more attention in biology and bioinformatics. They play vital roles in biological processes such as transcription and translation. Classification of ncRNAs is essential to our understanding of disease mechanisms and treatment design. Many approaches to ncRNA classification have been developed, several of which use machine learning and deep learning. In this paper, we construct a novel deep learning-based architecture, ncRDense, to effectively classify and distinguish ncRNA families. In a comparative study, our model produces comparable results with existing state-of-the-art methods. Finally, we built a freely accessible web server for the ncRDense tool, which is available at http://nsclbio.jbnu.ac.kr/tools/ncRDense/.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Classification; Deep learning; Densenet; Feature encoding; Non-coding RNA

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Year:  2021        PMID: 34242708     DOI: 10.1016/j.ygeno.2021.07.004

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  1 in total

1.  Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants.

Authors:  Dong Xu; Wenya Yuan; Chunjie Fan; Bobin Liu; Meng-Zhu Lu; Jin Zhang
Journal:  Front Plant Sci       Date:  2022-04-14       Impact factor: 6.627

  1 in total

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