DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes
Abstract Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model t...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-59487-5 |
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| author | Leonardo Ledesma-Dominguez Erik Carbajal-Degante Gabriel Moreno-Hagelsieb Ernesto Pérez-Rueda |
| author_facet | Leonardo Ledesma-Dominguez Erik Carbajal-Degante Gabriel Moreno-Hagelsieb Ernesto Pérez-Rueda |
| author_sort | Leonardo Ledesma-Dominguez |
| collection | DOAJ |
| description | Abstract Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model to identify transcription factors (TFs) among prokaryotic and eukaryotic protein sequences, named Deep Regulation (DeepReg) model. Two architectures were used in the DL model: a convolutional neural network (CNN), and a bidirectional long-short-term memory (BiLSTM). DeepReg reached a precision of 0.99, a recall of 0.97, and an F1-score of 0.98. The quality of our predictions, the bias-variance trade-off approach, and the characterization of new TF predictions were evaluated and compared against those produced by DeepTFactor, as well as against experimental data from three model organisms. Predictions based on our DLM tended to exhibit less variance and bias than those from DeepTFactor, thus increasing reliability and decreasing overfitting. |
| format | Article |
| id | doaj-art-a57e838fde374b9aa17f0e0e5b4b5edf |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a57e838fde374b9aa17f0e0e5b4b5edf2024-11-17T12:21:24ZengNature PortfolioScientific Reports2045-23222024-04-0114111110.1038/s41598-024-59487-5DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomesLeonardo Ledesma-Dominguez0Erik Carbajal-Degante1Gabriel Moreno-Hagelsieb2Ernesto Pérez-Rueda3Posgrado en Ciencia en Ingeniería de la Computación, Universidad Nacional Autónoma de MéxicoCoordinación de Universidad Abierta, Innovación Educativa y Educación a Distancia (CUAIEED), Universidad Nacional Autónoma de MéxicoDepartment of Biology, Wilfrid Laurier UniversityInstituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Unidad Académica del Estado de Yucatán, Universidad Nacional Autónoma de MéxicoAbstract Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model to identify transcription factors (TFs) among prokaryotic and eukaryotic protein sequences, named Deep Regulation (DeepReg) model. Two architectures were used in the DL model: a convolutional neural network (CNN), and a bidirectional long-short-term memory (BiLSTM). DeepReg reached a precision of 0.99, a recall of 0.97, and an F1-score of 0.98. The quality of our predictions, the bias-variance trade-off approach, and the characterization of new TF predictions were evaluated and compared against those produced by DeepTFactor, as well as against experimental data from three model organisms. Predictions based on our DLM tended to exhibit less variance and bias than those from DeepTFactor, thus increasing reliability and decreasing overfitting.https://doi.org/10.1038/s41598-024-59487-5 |
| spellingShingle | Leonardo Ledesma-Dominguez Erik Carbajal-Degante Gabriel Moreno-Hagelsieb Ernesto Pérez-Rueda DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes Scientific Reports |
| title | DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes |
| title_full | DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes |
| title_fullStr | DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes |
| title_full_unstemmed | DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes |
| title_short | DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes |
| title_sort | deepreg a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes |
| url | https://doi.org/10.1038/s41598-024-59487-5 |
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