SPREAD: An ensemble predictor based on DNA autoencoder framework for discriminating promoters in <i>Pseudomonas aeruginosa</i>

Shengming Zhou; Jia Zheng; Cangzhi Jia; Shengming Zhou; Jia Zheng; Cangzhi Jia

doi:10.3934/mbe.2022622

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 13294-13305. doi: 10.3934/mbe.2022622

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Research article Special Issues

SPREAD: An ensemble predictor based on DNA autoencoder framework for discriminating promoters in Pseudomonas aeruginosa

School of Science, Dalian Maritime University, Dalian 116026, China

Academic Editor: Wenrui Hao

Received: 04 July 2022 Revised: 23 August 2022 Accepted: 29 August 2022 Published: 13 September 2022

Regulatory elements in DNA sequences, such as promoters, enhancers, terminators and so on, are essential for gene expression in physiological and pathological processes. A promoter is the specific DNA sequence that is located upstream of the coding gene and acts as the "switch" for gene transcriptional regulation. Lots of promoter predictors have been developed for different bacterial species, but only a few are designed for Pseudomonas aeruginosa, a widespread Gram-negative conditional pathogen in nature. In this work, an ensemble model named SPREAD is proposed for the recognition of promoters in Pseudomonas aeruginosa. In SPREAD, the DNA sequence autoencoder model LSTM is employed to extract potential sequence information, and the mean output probability value of CNN and RF is applied as the final prediction. Compared with G4PromFinder, the only state-of-the-art classifier for promoters in Pseudomonas aeruginosa, SPREAD improves the prediction performance significantly, with an accuracy of 0.98, recall of 0.98, precision of 0.98, specificity of 0.97 and F1-score of 0.98.
- promoters,
- autoencoder model,
- machine learning,
- deep learning
Citation: Shengming Zhou, Jia Zheng, Cangzhi Jia. SPREAD: An ensemble predictor based on DNA autoencoder framework for discriminating promoters in Pseudomonas aeruginosa[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13294-13305. doi: 10.3934/mbe.2022622

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Abstract

Regulatory elements in DNA sequences, such as promoters, enhancers, terminators and so on, are essential for gene expression in physiological and pathological processes. A promoter is the specific DNA sequence that is located upstream of the coding gene and acts as the "switch" for gene transcriptional regulation. Lots of promoter predictors have been developed for different bacterial species, but only a few are designed for Pseudomonas aeruginosa, a widespread Gram-negative conditional pathogen in nature. In this work, an ensemble model named SPREAD is proposed for the recognition of promoters in Pseudomonas aeruginosa. In SPREAD, the DNA sequence autoencoder model LSTM is employed to extract potential sequence information, and the mean output probability value of CNN and RF is applied as the final prediction. Compared with G4PromFinder, the only state-of-the-art classifier for promoters in Pseudomonas aeruginosa, SPREAD improves the prediction performance significantly, with an accuracy of 0.98, recall of 0.98, precision of 0.98, specificity of 0.97 and F1-score of 0.98.

References

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