论文标题
使用深卷积神经网络对非编码RNA元件进行分类
Classification of Noncoding RNA Elements Using Deep Convolutional Neural Networks
论文作者
论文摘要
本文提议采用深层卷积神经网络（CNN）来对非编码RNA（NCRNA）序列进行分类。为此，我们首先提出了一种有效的方法，将RNA序列转换为表征其碱基对概率的图像。结果，将RNA序列分类转换为图像分类问题，该问题可以通过可用的基于CNN的分类模型有效地解决。本文还考虑了NCRNA的折叠潜力，除了它们的主要序列。基于提出的方法，从NCRNA序列的RFAM数据库生成基准图像分类数据集。此外，已经实施了三个经典的CNN模型并进行了比较，以证明所提出方法的出色性能和效率。广泛的实验结果表明，将深度学习方法用于RNA分类的巨大潜力。
The paper proposes to employ deep convolutional neural networks (CNNs) to classify noncoding RNA (ncRNA) sequences. To this end, we first propose an efficient approach to convert the RNA sequences into images characterizing their base-pairing probability. As a result, classifying RNA sequences is converted to an image classification problem that can be efficiently solved by available CNN-based classification models. The paper also considers the folding potential of the ncRNAs in addition to their primary sequence. Based on the proposed approach, a benchmark image classification dataset is generated from the RFAM database of ncRNA sequences. In addition, three classical CNN models have been implemented and compared to demonstrate the superior performance and efficiency of the proposed approach. Extensive experimental results show the great potential of using deep learning approaches for RNA classification.
