Open AccessSPREAD: An ensemble predictor based on DNA autoencoder framework for discriminating promoters in <i>Pseudomonas aeruginosa</i>
Author(s) -
Shengming Zhou,
Juanjuan Zheng,
Cangzhi Jia
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022622
Subject(s) - promoter , biology , gene , pseudomonas aeruginosa , genetics , dna sequencing , enhancer , dna , autoencoder , computational biology , gene expression , artificial intelligence , computer science , deep learning , bacteria
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.