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

Journal: Genomics
Published Date: Jul 7, 2021

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/.

Authors

  • Tuvshinbayar Chantsalnyam
    Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
  • Arslan Siraj
    Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
  • Hilal Tayara
    Department of Electronics and Information Engineering, Chonbuk National University, Jeonju 54896, South Korea. Electronic address: hilaltayara@jbnu.ac.kr.
  • Kil To Chong
    Division of Electronic Engineering, and Advanced Research Center of Electronics and Information, Chonbuk National University, Jeonju-Si 54896, South Korea. Electronic address: kitchong@jbnu.ac.kr.

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