IEEE/ACM Trans Comput Biol Bioinform. 2019 Jul-Aug;16(4):1184-1192. doi: 10.1109/TCBB.2018.2819660. Epub 2018 Mar 26.
Although Deep learning algorithms have outperformed conventional methods in predicting the sequence specificities of DNA-protein binding, they lack to consider the dependencies among nucleotides and the diverse binding lengths for different transcription factors (TFs). To address the above two limitations simultaneously, in this paper, we propose a high-order convolutional neural network architecture (HOCNN), which employs a high-order encoding method to build high-order dependencies among nucleotides, and a multi-scale convolutional layer to capture the motif features of different length. The experimental results on real ChIP-seq datasets show that the proposed method outperforms the state-of-the-art deep learning method (DeepBind) in the motif discovery task. In addition, we provide further insights about the importance of introducing additional convolutional kernels and the degeneration problem of importing high-order in the motif discovery task.
虽然深度学习算法在预测 DNA-蛋白质结合的序列特异性方面已经超过了传统方法，但它们缺乏对核苷酸之间的依赖关系以及不同转录因子 (TF) 的不同结合长度的考虑。为了同时解决上述两个限制，在本文中，我们提出了一种高阶卷积神经网络架构（HOCNN），它采用高阶编码方法来构建核苷酸之间的高阶依赖关系，并采用多尺度卷积层来捕获不同长度的基序特征。在真实的 ChIP-seq 数据集上的实验结果表明，该方法在基序发现任务中优于最先进的深度学习方法（DeepBind）。此外，我们还进一步探讨了在基序发现任务中引入额外卷积核的重要性以及引入高阶的退化问题。
