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

被引：12

作者：

Chantsalnyam, Tuvshinbayar ^{[1
]}

Siraj, Arslan ^{[1
]}

Tayara, Hilal ^{[2
]}

Chong, Kil To ^{[1
,3
]}

机构：

[1] Jeonbuk Natl Univ, Dept Elect & Informat Engn, Jeonju 54896, South Korea

[2] Jeonbuk Natl Univ, Sch Int Engn & Sci, Jeonju 54896, South Korea

[3] Jeonbuk Natl Univ, Adv Elect & Informat Res Ctr, Jeonju 54896, South Korea

来源：

GENOMICS | 2021年 / 113卷 / 05期

基金：

新加坡国家研究基金会;

关键词：

Deep learning; Densenet; Classification; Non-coding RNA; Feature encoding; IDENTIFICATION;

D O I：

10.1016/j.ygeno.2021.07.004

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 ; 0836 ; 090102 ; 100705 ;

摘要：

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/.

引用

页码：3030 / 3038

页数：9