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Identification and classification of promoters using the attention mechanism based on long short-term memory

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Abstract

A promoter is a short region of DNA that can bind RNA polymerase and initiate gene transcription. It is usually located directly upstream of the transcription initiation site. DNA promoters have been proven to be the main cause of many human diseases, especially diabetes, cancer or Huntington’s disease. Therefore, the classification of promoters has become an interesting problem and has attracted the attention of many researchers in the field of bioinformatics. Various studies have been conducted in order to solve this problem, but their performance still needs further improvement. In this research, we segmented the DNA sequence in a k-mers manner, then trained the word vector model, inputted it into long short-term memory(LSTM) and used the attention mechanism to predict. Our method can achieve 93.45% and 90.59% cross-validation accuracy in the two layers, respectively. Our results are better than others based on the same data set, and provided some ideas for accurately predicting promoters. In addition, this research suggested that natural language processing can play a significant role in biological sequence prediction.

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Acknowledgements

The classifiers in this article were provided by the WEKA platform. This research was funded by the Natural Science Foundation of China (Grant No. 61902259), the Natural Science Foundation of Guangdong province (2018A0303130084). The authors declare no conflict of interest.

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Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Qingwen Li & Quan Zou

  2. State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China

    Qingwen Li

  3. Forestry and Fruit Tree Research Institute, Wuhan Academy of Agricultural Sciences, Wuhan, 430075, China

    Qingyuan Li

  4. School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, 518172, China

    Lichao Zhang

  5. School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, 518055, China

    Lei Xu

  6. School of Management, Shenzhen Polytechnic, Shenzhen, 518055, China

    Jin Wu

Authors
  1. Qingwen Li
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  2. Lichao Zhang
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  3. Lei Xu
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  4. Quan Zou
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  5. Jin Wu
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  6. Qingyuan Li
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Corresponding authors

Correspondence to Jin Wu or Qingyuan Li.

Additional information

Qingwen Li is a doctoral student at the Institute of Biophysics, Chinese Academy of Sciences, China. He once visited the University of Electronic Science and Technology of China, China. His research interests include bioinformatics and neurobiology.

Lichao Zhang is a lecturer at the School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, China. Her research interests include machine learning and bioinformatics.

Lei Xu is an associate professor at the School of Electronic and Communication Engineering, Shenzhen Polytechnic, China. Her research interests include machine learning and bioinformatics.

Quan Zou is a professor at the University of Electronic Science and Technology of China, China. He is a senior member of IEEE and ACM. He won the Clarivate Analytics Highly Cited Researchers in 2018 and 2019. He majors in bioinformatics, machine learning, and algorithms.

Jin Wu is a lecture at the School of Management, Shenzhen Polytechnic. China. Her research interests include microbiome and bioinformatics.

Qingyuan Li is a senior engineer of Forestry and Fruit Tree Research Institute, Wuhan Academy of Agricultural Sciences, China. His research interests include plant genetics, plant genomics and bioinformatics.

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Li, Q., Zhang, L., Xu, L. et al. Identification and classification of promoters using the attention mechanism based on long short-term memory. Front. Comput. Sci. 16, 164348 (2022). https://doi.org/10.1007/s11704-021-0548-9

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