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A New Method Combining DNA Shape Features to Improve the Prediction Accuracy of Transcription Factor Binding Sites

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Intelligent Computing Theories and Application (ICIC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12464))

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Abstract

Identifying transcription factor (TF) binding sites (TFBSs) has play an important role in the computational inference of gene regulation. With the development of high-throughput technologies, there have been many conventional methods and deep learning models used in the identification of TFBSs. However, most methods are designed to predict TFBSs only based on raw DNA sequence leads to low accuracy. Therefore, we propose a Dual-channel Convolutional neural network (CNN) model combining DNA sequences and DNA Shape features to predict TFBSs, named DCDS. In the DCDS model, the convolution layer captures low-level features from input data and parallel pooling operations are used to find the most significant activation signal in a sequence for each filter to improve the prediction accuracy of TFBSs. We conduct a series of experiments on 66 in vitro datasets and experimental results show that proposed model DCDS is superior to some state-of-the-art methods.

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Acknowledgments

This work was supported by the grant of National Key R&D Program of China (No. 2018YFA0902600 & 2018AAA0100100) and partly supported by National Natural Science Foundation of China (Grant nos. 61861146002, 61520106006, 61772370, 61702371, 61732012, 61932008, 61532008, 61672382, 61772357, and 61672203) and China Postdoctoral 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 and supported by Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), LCNBI and ZJLab.

Author information

Authors and Affiliations

  1. The Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Siguo Wang, Zhen Shen, Ying He & Qinhu Zhang

  2. Guangxi Academy of Science, Nanning, 530025, China

    Changan Yuan

  3. School of Computer and Information Engineering, Nanning Normal University, Nanning, 530299, China

    Xiao Qin

  4. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China

    Hongjie Wu

  5. Institute of Science and Technology for Brain Inspired Intelligence (ISTBI), Fudan University, Shanghai, 200433, China

    Xingming Zhao

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  1. Siguo Wang
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  3. Ying He
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  6. Xiao Qin
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Correspondence to Qinhu Zhang .

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Editors and Affiliations

  1. Institute of Machine Learning and Systems Biology, Tongji University, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

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Wang, S. et al. (2020). A New Method Combining DNA Shape Features to Improve the Prediction Accuracy of Transcription Factor Binding Sites. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-60802-6_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-60802-6

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