Recurrent Neural Network for Predicting Transcription Factor Binding Sites.

It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome these problems, some computational methods, based on k-mer features or convolutional neural networks, have been proposed to identify TF binding sites from DNA sequences. Although these methods have good performance, the context information that relates to TF binding sites is still lacking. Research indicates that standard recurrent neural networks (RNN) and its variants have better performance in time-series data compared with other models. In this study, we propose a model, named KEGRU, to identify TF binding sites by combining Bidirectional Gated Recurrent Unit (GRU) network with k-mer embedding. Firstly, DNA sequences are divided into k-mer sequences with a specified length and stride window. And then, we treat each k-mer as a word and pre-trained word representation model though word2vec algorithm. Thirdly, we construct a deep bidirectional GRU model for feature learning and classification. Experimental results have shown that our method has better performance compared with some state-of-the-art methods. Additional experiments about embedding strategy show that k-mer embedding will be helpful to enhance model performance. The robustness of KEGRU is proved by experiments with different k-mer length, stride window and embedding vector dimension.

众所周知，DNA序列包含一定量的转录因子（TF）结合位点，并且仅通过生物学实验鉴定了其中的一部分。但是，这些实验既昂贵又耗时。为了克服这些问题，已经提出了一些基于K-MER特征或卷积神经网络的计算方法，以从DNA序列中识别TF结合位点。尽管这些方法具有良好的性能，但仍然缺乏与TF绑定站点有关的上下文信息。研究表明，与其他模型相比，标准复发性神经网络（RNN）及其变体在时间序列数据中具有更好的性能。在这项研究中，我们提出了一个名为Kegru的模型，以通过将双向门控复发单元（GRU）网络与K-MER嵌入相结合，以识别TF结合位点。首先，将DNA序列分为具有指定长度和步幅窗口的K-MER序列。然后，我们将每个K-MER视为单词和预训练的单词表示模型，该模型是Word2Vec算法。第三，我们为特征学习和分类构建了深层双向GRU模型。实验结果表明，与某些最新方法相比，我们的方法具有更好的性能。有关嵌入策略的其他实验表明，K-MER嵌入将有助于提高模型性能。 Kegru的鲁棒性通过具有不同的K-MER长度，步幅窗口和嵌入向量维度的实验证明。