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Using Deep Learning to Predict Transcription Factor Binding Sites Based on Multiple-omics Data

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Intelligent Computing Theories and Application (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13393))

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Abstract

Transcription factors (TFs) have a great effect on gene transcription process. TFs can boost the formation of complex gene expression regulation system by promoting or inhibiting gene binding to DNA, which is called as TF binding sites (TFBSs). Recent years have seen the rapid development deep learning (DL) method in natural language processing (NLP), computer vision (CV) and these methods outperform than the state-of-the-art method. Many scholars applied these methods to motif discovery, e.g., DeepBind and DenQ. But these methods only use the raw DNA sequence as input data. Instead of improving complex model, massive biological data brought by high-throughput sequencing technology provides a different idea. In this paper, we propose a simple and effective DL-based model, namely DeepCR, integrating multiple-omics data to predict TFBSs. Experiments on 21 motif datasets of GM12878 cell line from in-vitro protein binding microarray data show that multiple-omics data can significantly improve the overall performance. More specifically, the average AUC is improved by 3.89% for histone modifications, and 3.77% for MeDIP-seq respectively, and 6.63% for histone modifications and MeDIP-seq together. And the mean AR is increased by 3.90% for histone modifications, and 4.50% for MeDIP-seq respectively, and 6.00% for histone modifications and MeDIP-seq together.

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Acknowledgements

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. 61732012, 62002266, 61932008, and 62073231), and Introduction Plan of High-end Foreign Experts (Grant no. G2021033002L) and, respectively, supported by the Key Project of Science and Technology of Guangxi (Grant no. 2021AB20147), Guangxi Natural Science Foundation (Grant nos. 2021JJA170204 & 2021JJA170199) and Guangxi Science and Technology Base and Talents Special Project (Grant nos. 2021AC19354 & 2021AC19394).

Author information

Authors and Affiliations

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

    Youhong Xu

  2. Guangxi Academy of Science, Nanning, 530007, China

    Changan Yuan

  3. Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy Sciences, Nanning, 530001, China

    Changan Yuan

  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

Authors
  1. Youhong Xu
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  2. Changan Yuan
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  3. Hongjie Wu
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  4. Xingming Zhao
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Corresponding author

Correspondence to Youhong Xu .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Xi'an Polytechnic University, Xi'an, China

    Junfeng Jing

  4. The University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  5. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  6. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Xu, Y., Yuan, C., Wu, H., Zhao, X. (2022). Using Deep Learning to Predict Transcription Factor Binding Sites Based on Multiple-omics Data. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_65

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  • DOI: https://doi.org/10.1007/978-3-031-13870-6_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13869-0

  • Online ISBN: 978-3-031-13870-6

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