| Literature DB >> 34242708 |
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/.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