pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters

Shujaat, Muhammad; Wahab, Abdul; Tayara, Hilal; Chong, Kil To

doi:10.3390/genes11121529

Cited by 39 publications

(37 citation statements)

References 40 publications

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“…The ongoing advancements in CNNs has made them highly reliable, and these networks have achieved novel results in various fields. CNNs have also achieved remarkable results in the area of medical image processing [29], [33], [34] and bioinformatics [35], [36]. However, numerous notable examples use CNNs to build predictors that can detect the variation occurred in genetic sequence.…”

Section: B Model Architecturementioning

confidence: 99%

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Cr-Prom: A Convolutional Neural Network-Based Model for the Prediction of Rice Promoters

et al. 2021

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The promoter is a regulatory region of the DNA typically located upstream of a gene and plays a key role in regulating gene transcription. Accurate prediction of promoters is crucial for the analysis of gene expression patterns and for the development and understanding of genetic regulatory networks. Genomes of several species have been sequenced, and their gene content has been established to a large extent. Some bioinformatics algorithms have been developed for predicting promoters with high universality for all kinds of plants; however, few studies have been conducted to identify promoters in rice, which might affect the practical applications. Here, we present a rice promoter prediction tool, Cr-Prom. This predictor has been established using a series of sequence-based features and datasets extracted from the PlantProm and RAP-DB databases. We applied a convolutional neural network (CNN)-based strategy to construct a predictor with robust classification performance. To demonstrate our dominance, we ran experiments on a benchmark dataset using 5-fold cross-validation and compared our results with existing techniques using four figure of merits. In addition, CR-Prom was analyzed on an independent dataset. Based on the results, Cr-Prom outperformed the existing rice-specific promoter predictors.

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Section: B Model Architecturementioning

confidence: 99%

“…We used broadly applied methodological measures [14], [36], [39]- [41] to comprehensively analyze the efficiency of the promoter's prediction. These include Matthew's correlation coefficient (MCC), accuracy (Acc), sensitivity (Sn), specificity (Sp), and ROC curve.…”

Section: A Evaluation Parametermentioning

confidence: 99%

Cr-Prom: A Convolutional Neural Network-Based Model for the Prediction of Rice Promoters

et al. 2021

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“…However, these large-scale experimental screening techniques for the identification of ubiquitination sites are time consuming, expensive, and laborious. Owing to the advantages and emergence of machine learning models, they have been utilized in different fields, such as natural language processing (NLP) [ 28 , 29 ], energy load forecasting [ 30 ], speech recognition [ 31 ], image recognition [ 32 , 33 , 34 ], and computational biology [ 35 , 36 , 37 , 38 ]. Computational predictors were built to predict ubiquitination sites in a cost- and time-effective manner.…”

Section: Introductionmentioning

confidence: 99%

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

Siraj

Lim

Tayara

et al. 2021

Genes

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Protein ubiquitylation is an essential post-translational modification process that performs a critical role in a wide range of biological functions, even a degenerative role in certain diseases, and is consequently used as a promising target for the treatment of various diseases. Owing to the significant role of protein ubiquitylation, these sites can be identified by enzymatic approaches, mass spectrometry analysis, and combinations of multidimensional liquid chromatography and tandem mass spectrometry. However, these large-scale experimental screening techniques are time consuming, expensive, and laborious. To overcome the drawbacks of experimental methods, machine learning and deep learning-based predictors were considered for prediction in a timely and cost-effective manner. In the literature, several computational predictors have been published across species; however, predictors are species-specific because of the unclear patterns in different species. In this study, we proposed a novel approach for predicting plant ubiquitylation sites using a hybrid deep learning model by utilizing convolutional neural network and long short-term memory. The proposed method uses the actual protein sequence and physicochemical properties as inputs to the model and provides more robust predictions. The proposed predictor achieved the best result with accuracy values of 80% and 81% and F-scores of 79% and 82% on the 10-fold cross-validation and an independent dataset, respectively. Moreover, we also compared the testing of the independent dataset with popular ubiquitylation predictors; the results demonstrate that our model significantly outperforms the other methods in prediction classification results.

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“…In recent years, neural networks have acquired great importance due to their high performance in different fields like medical imaging [29][30][31], agriculture [32][33][34][35][36][37], image quality assessment and others. Moreover, neural networks have exhibited great performance in the identification of 6mA sites [38,39], m6A sites [40,41], 4mC sites [23,24,42], functional piRNAs [43], N4-acetylcytidine sites [44], promoters classification [45] and others. Inspired from the high performance given by neural network tools for modification identification in different sites, we have proposed a neural network based tool for 4mC identification in Rosaceae genomes.…”

Section: Introductionmentioning

confidence: 99%

iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

2021

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DNA N4-Methylcytosine is a genetic modification process which has an essential role in changing different biological processes such as DNA conformation, DNA replication, DNA stability, cell development and structural alteration in DNA. Due to its negative effects, it is important to identify the modified 4mC sites. Further, methylcytosine may develop anywhere at cytosine residue, however, clonal gene expression patterns are most likely transmitted just for cytosine residues in strand-symmetrical sequences. For this reason many different experiments are introduced but they proved not to be viable choice due to time limitation and high expenses. Therefore, to date there is still need for an efficient computational method to deal with 4mC sites identification. Keeping it in mind, in this research we have proposed an efficient model for Fragaria vesca (F. vesca) and Rosa chinensis (R. chinensis) genome. The proposed iRG-4mC tool is developed based on neural network architecture with two encoding schemes to identify the 4mC sites. The iRG-4mC predictor outperformed the existing state-of-the-art computational model by an accuracy difference of 9.95% on F. vesca (training dataset), 8.7% on R. chinesis (training dataset), 6.2% on F. vesca (independent dataset) and 10.6% on R. chinesis (independent dataset). We have also established a webserver which is freely accessible for the research community.

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pcPromoter-CNN: A CNN-Based Prediction and Classification of Promoters

Cited by 39 publications

References 40 publications

Cr-Prom: A Convolutional Neural Network-Based Model for the Prediction of Rice Promoters

Cr-Prom: A Convolutional Neural Network-Based Model for the Prediction of Rice Promoters

UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites

iRG-4mC: Neural Network Based Tool for Identification of DNA 4mC Sites in Rosaceae Genome

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