Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery task and have achieved state-of–art performance. However, due to the limitations of CNN, motif discovery methods based on CNN do not take full advantage of large-scale sequencing data generated by high-throughput sequencing technology. Hence, in this paper we propose multi-scale capsule network architecture (MSC) integrating multi-scale CNN, a variant of CNN able to extract motif features of different lengths, and capsule network, a novel type of artificial neural network architecture aimed at improving CNN. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea.

中文翻译：

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用于预测 DNA 蛋白结合位点的多尺度胶囊网络

发现 DNA-蛋白质结合位点，也称为基序发现，是进一步分析转录因子 (TF) 的基础。卷积神经网络 (CNN) 等深度学习算法已被引入到主题发现任务中，并取得了最先进的性能。然而，由于 CNN 的局限性，基于 CNN 的基序发现方法并没有充分利用高通量测序技术产生的大规模测序数据。因此，在本文中，我们提出了多尺度胶囊网络架构（MSC），它集成了多尺度 CNN（一种能够提取不同长度的主题特征的 CNN 变体）和胶囊网络（一种旨在改进的新型人工神经网络架构）。美国有线电视新闻网。