Abstract
With the rapid development of deep learning, some discriminative motif discovery methods based on deep neural network are gradually becoming the mainstream, which also bringing huge improvement of prediction accuracy. In this paper, we propose a convolutional neural network based architecture (eCNN), combining embedding layer with GloVe. Firstly, eCNN divides each single sequence of ChIP-seq datasets into multiple subsequences called k-mers by a sliding window, and then encoding k-mers into a relatively low dimension vectors by GloVe, and finally scores each vector using multiple convolutional networks. The experiment shows that our architecture can get good results on the task of motif discovery.
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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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Wang, D., Zhang, Q., Yuan, CA., Qin, X., Huang, ZK., Shang, L. (2019). Motif Discovery via Convolutional Networks with K-mer Embedding. 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_36
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