Chantsalnyam Tuvshinbayar, Siraj Arslan, Tayara Hilal, Chong Kil To
Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, South Korea.
Genomics. 2021 Sep;113(5):3030-3038. doi: 10.1016/j.ygeno.2021.07.004. Epub 2021 Jul 7.
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/.
随着生物学研究的重要性迅速增长,非编码RNA(ncRNA)在生物学和生物信息学领域吸引了更多关注。它们在转录和翻译等生物学过程中发挥着至关重要的作用。ncRNA的分类对于我们理解疾病机制和治疗设计至关重要。已经开发了许多ncRNA分类方法,其中一些方法使用机器学习和深度学习。在本文中,我们构建了一种基于深度学习的新型架构ncRDense,以有效地对ncRNA家族进行分类和区分。在一项比较研究中,我们的模型与现有的最先进方法产生了可比的结果。最后,我们为ncRDense工具构建了一个可免费访问的网络服务器,可在http://nsclbio.jbnu.ac.kr/tools/ncRDense/获取。