Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea.
Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, South Korea; Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, South Korea.
Comput Biol Chem. 2020 Oct;88:107364. doi: 10.1016/j.compbiolchem.2020.107364. Epub 2020 Aug 27.
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.
非编码 RNA（ncRNA）是一种不会转化为蛋白质的 RNA，但它参与了许多生物过程、疾病和癌症。高通量测序技术已经鉴定和分类了许多 ncRNA。因此，准确的 ncRNA 分类预测对于进一步研究其功能是重要且必要的。已经采用了几种计算技术来预测 ncRNA 的类别。最近的分类方法将二级结构用作其主要输入。然而，RNA 二级结构的计算工具不够准确，这会影响 ncRNA 预测器的最终性能。在本文中，我们提出了一种简单而有效的方法，称为 ncRDeep，用于 ncRNA 预测。它仅使用简单的卷积神经网络和 RNA 序列信息。在基准数据集上对 ncRDeep 进行了评估，比较结果表明，ncRDeep 显著优于最先进的方法。更具体地说，平均准确率提高了 8.32%。最后，我们在 http://home.jbnu.ac.kr/NSCL/ncRDeep.htm 上为开发的工具 ncRDeep 构建了一个免费的可访问的网络服务器。
