Abstract
Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analyses of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN) are introduced to motif discovery task and have achieved state-of–art performance. However, these methods still have limitations such as neglecting the context information in large-scale sequencing data. Thus, inspired by the similarity between DNA sequence and human language, in this paper we propose a hierarchical attention network for predicting DNA-protein binding sites which is based on a natural language processing method for document classification. 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.
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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61861146002, 61520106006, 61772370, 61873270, 61702371, 61672382, 61672203, 61572447, 61772357, and 61732012, China Post-doctoral Science Foundation Grant, No. 2017M611619, and supported by “BAGUI Scholar” Program and the Scientific & Technological Base and Talent Special Program, GuiKe AD18126015 of the Guangxi Zhuang Autonomous Region of China.
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Yu, W., Yuan, CA., Qin, X., Huang, ZK., Shang, L. (2019). Hierarchical Attention Network for Predicting DNA-Protein Binding Sites. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_35
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DOI: https://doi.org/10.1007/978-3-030-26969-2_35
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