NcRDeep: Non-coding RNA classification with convolutional neural network. (October 2020)

• Record Type: Journal Article
• Authors: Chantsalnyam, Tuvshinbayar; Lim, Dae Yeong; Tayara, Hilal; Chong, Kil To
• Is Part Of: Computational biology and chemistry. Volume 88(2020)
• Publication Date: 2020-10
• Subjects: Convolution neural network -- Classification -- Deep learning -- Non-coding RNA; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2020.107364
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: Computational predictor has been developed for the prediction of the types of ncRNA. A simple and efficient deep learning approach has been used. Webserver for the developed tool has been built and made freely accessible. The proposed tool has achieved promising outcomes compared with existing methods. Abstract: A non-coding RNA (ncRNA) is a kind of RNA that is not converted into protein, however, it is involved in many biological processes, diseases, and cancers. Numerous ncRNAs have been identified and classified with high throughput sequencing technology. Hence, accurate ncRNAs class prediction is important and necessary for further study of their functions. Several computation techniques have been employed to predict the class of ncRNAs. Recent classification methods used the secondary structure as their primary input. However, the computational tools of RNA secondary structure are not accurate enough which affects the final performance of ncRNAs predictors. In this paper, we propose a simple yet efficient method, called ncRDeep, for ncRNAs prediction. It uses a simple convolutional neural network and RNA sequence information only. The ncRDeep was evaluated on benchmark datasets and the comparison results showed that the ncRDeep outperforms the state-of-the-art methods significantly. More specifically, the average accuracy was improved by 8.32%. Finally, we built a freely accessible web server for the developed tool ncRDeep at http://home.jbnu.ac.kr/NSCL/ncRDeep.htm