pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters
A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction...
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MDPI AG
2020-12-01
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| Series: | Genes |
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| Online Access: | https://www.mdpi.com/2073-4425/11/12/1529 |
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doaj-d379696b02b14e208d516ca76f5d10662020-12-22T00:05:58ZengMDPI AGGenes2073-44252020-12-01111529152910.3390/genes11121529pcPromoter-CNN: A CNN-Based Prediction and Classification of PromotersMuhammad Shujaat0Abdul Wahab1Hilal Tayara2Kil To Chong3Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaSchool of International Engineering and Science, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, KoreaA promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools.https://www.mdpi.com/2073-4425/11/12/1529bioinformaticscomputational biologyconvolution neural network (CNN)promotersnon-promoters |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Muhammad Shujaat Abdul Wahab Hilal Tayara Kil To Chong |
| spellingShingle |
Muhammad Shujaat Abdul Wahab Hilal Tayara Kil To Chong pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters Genes bioinformatics computational biology convolution neural network (CNN) promoters non-promoters |
| author_facet |
Muhammad Shujaat Abdul Wahab Hilal Tayara Kil To Chong |
| author_sort |
Muhammad Shujaat |
| title |
pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters |
| title_short |
pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters |
| title_full |
pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters |
| title_fullStr |
pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters |
| title_full_unstemmed |
pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters |
| title_sort |
pcpromoter-cnn: a cnn-based prediction and classification of promoters |
| publisher |
MDPI AG |
| series |
Genes |
| issn |
2073-4425 |
| publishDate |
2020-12-01 |
| description |
A promoter is a small region within the DNA structure that has an important role in initiating transcription of a specific gene in the genome. Different types of promoters are recognized by their different functions. Due to the importance of promoter functions, computational tools for the prediction and classification of a promoter are highly desired. Promoters resemble each other; therefore, their precise classification is an important challenge. In this study, we propose a convolutional neural network (CNN)-based tool, the pcPromoter-CNN, for application in the prediction of promotors and their classification into subclasses σ70, σ54, σ38, σ32, σ28 and σ24. This CNN-based tool uses a one-hot encoding scheme for promoter classification. The tools architecture was trained and tested on a benchmark dataset. To evaluate its classification performance, we used four evaluation metrics. The model exhibited notable improvement over that of existing state-of-the-art tools. |
| topic |
bioinformatics computational biology convolution neural network (CNN) promoters non-promoters |
| url |
https://www.mdpi.com/2073-4425/11/12/1529 |
| work_keys_str_mv |
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