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Article

PromoterLCNN: A Light CNN-Based Promoter Prediction and Classification Model

1
Department of Electronics Engineering, Universidad Técnica Federico Santa María, Valparaiso 2390123, Chile
2
Laboratorio de Microbiología Molecular y Biotecnología Ambiental, Departamento de Química & Centro de Biotecnología Daniel Alkalay Lowitt, Universidad Técnica Federico Santa María, Valparaiso 2390123, Chile
3
Department of Informatics Engineering, Universidad Técnica Federico Santa María, Valparaiso 2390123, Chile
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Piero Fariselli
Genes 2022, 13(7), 1126; https://doi.org/10.3390/genes13071126
Received: 23 May 2022 / Revised: 15 June 2022 / Accepted: 20 June 2022 / Published: 23 June 2022
(This article belongs to the Section Bioinformatics)
Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have been applied to identify and classify bacterial promoters recognized by sigma (σ) factors and RNA polymerase subunits which increase affinity to specific DNA sequences to modulate transcription and respond to nutritional or environmental changes. This work presents a new multiclass promoter prediction model by using convolutional neural networks (CNNs), denoted as PromoterLCNN, which classifies Escherichia coli promoters into subclasses σ70, σ24, σ32, σ38, σ28, and σ54. We present a light, fast, and simple two-stage multiclass CNN architecture for promoter identification and classification. Training and testing were performed on a benchmark dataset, part of RegulonDB. Comparative performance of PromoterLCNN against other CNN-based classifiers using four parameters (Acc, Sn, Sp, MCC) resulted in similar or better performance than those that commonly use cascade architecture, reducing time by approximately 30–90% for training, prediction, and hyperparameter optimization without compromising classification quality. View Full-Text
Keywords: bacterial promoters; convolutional neural networks; bioinformatics; deep learning; PromoterLCNN bacterial promoters; convolutional neural networks; bioinformatics; deep learning; PromoterLCNN
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MDPI and ACS Style

Hernández, D.; Jara, N.; Araya, M.; Durán, R.E.; Buil-Aranda, C. PromoterLCNN: A Light CNN-Based Promoter Prediction and Classification Model. Genes 2022, 13, 1126. https://doi.org/10.3390/genes13071126

AMA Style

Hernández D, Jara N, Araya M, Durán RE, Buil-Aranda C. PromoterLCNN: A Light CNN-Based Promoter Prediction and Classification Model. Genes. 2022; 13(7):1126. https://doi.org/10.3390/genes13071126

Chicago/Turabian Style

Hernández, Daryl, Nicolás Jara, Mauricio Araya, Roberto E. Durán, and Carlos Buil-Aranda. 2022. "PromoterLCNN: A Light CNN-Based Promoter Prediction and Classification Model" Genes 13, no. 7: 1126. https://doi.org/10.3390/genes13071126

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