Identification of promoters and their strength using deep learning

Tayara, Hilal; Oubounyt, Mhaned; Chong, Kil To

doi:10.1016/j.ibror.2019.07.1738

Cited by 3 publications

(3 citation statements)

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“…The NAT10, which is related to the mRNA down control, decreases ac4C detection at the mapped mRNA location. To establish the role of ac4C in the mRNA translation law, acetylated residues were thus added to the repertory of mRNA, and PACES data were amended [5][6][7][8][9][10][11][12]. Although mRNA vaccines are a promising solution to humanity's most recent grand challenge, the technology is still in its early stages.…”

Section: Introductionmentioning

confidence: 99%

“…Nuclear chemicals (NCP), nuclear density (DN), k-mer, single hot coding, and pseudopotential electron-ion interaction are among the data sets included in the benchmarking data set. DL-aC4C was calculated using a variety of parameters, including precision, sensitivity, and specificity, all of which are commonly used in the field of bioinformatics [8][9][10][11][12][13]. Crossvalidation with measurement parameters was used for DL-ac4C controls.…”

Section: Introductionmentioning

confidence: 99%

“…Crossvalidation with measurement parameters was used for DL-ac4C controls. Furthermore, there are uneven data sets [10,13] for the receiver operating curve (ROC) and the recall curve (PRC). As a result, the ROC and PRC are used to select the best vector representation and deep learning classification.…”

Section: Introductionmentioning

confidence: 99%

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Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

et al. 2022

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Deep learning models have been successfully applied in a wide range of fields. The creation of a deep learning framework for analyzing high-performance sequence data have piqued the research community’s interest. N4 acetylcytidine (ac4C) is a post-transcriptional modification in mRNA, is an mRNA component that plays an important role in mRNA stability control and translation. The ac4C method of mRNA changes is still not simple, time consuming, or cost effective for conventional laboratory experiments. As a result, we developed DL-ac4C, a CNN-based deep learning model for ac4C recognition. In the alternative scenario, the model families are well-suited to working in large datasets with a large number of available samples, especially in biological domains. In this study, the DL-ac4C method (deep learning) is compared to non-deep learning (machine learning) methods, regression, and support vector machine. The results show that DL-ac4C is more advanced than previously used approaches. The proposed model improves the accuracy recall area by 9.6 percent and 9.8 percent, respectively, for cross-validation and independent tests. More nuanced methods of incorporating prior bio-logical knowledge into the estimation procedure of deep learning models are required to achieve better results in terms of predictive efficiency and cost-effectiveness. Based on an experiment’s acetylated dataset, the DL-ac4C sequence-based predictor for acetylation sites in mRNA can predict whether query sequences have potential acetylation motifs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

et al. 2022

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ncRDeep: Non-coding RNA classification with convolutional neural network

Chantsalnyam

Lim

Tayara

et al. 2020

Computational Biology and Chemistry

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XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

Alam

Tayara

Chong

2020

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N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA which plays a major role in the stability and regulation of mRNA translation. The working mechanism of ac4C modification in mRNA is still unclear and traditional laboratory experiments are time-consuming and expensive. Therefore, we propose an XG-ac4C machine learning model based on the eXtreme Gradient Boost classifier for the identification of ac4C sites. The XG-ac4C model uses a combination of electron-ion interaction pseudopotentials and electron-ion interaction pseudopotentials of trinucleotide of the nucleotides in ac4C sites. Moreover, Shapley additive explanations and local interpretable model-agnostic explanations are applied to understand the importance of features and their contribution to the final prediction outcome. The obtained results demonstrate that XG-ac4C outperforms existing state-of-the-art methods. In more detail, the proposed model improves the area under the precision-recall curve by 9.4% and 9.6% in cross-validation and independent tests, respectively. Finally, a user-friendly web server based on the proposed model for ac4C site identification is made freely available at http://nsclbio.jbnu.ac.kr/tools/xgac4c/.

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Identification of promoters and their strength using deep learning

Cited by 3 publications

References 0 publications

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

Recognition of mRNA N4 Acetylcytidine (ac4C) by Using Non-Deep vs. Deep Learning

ncRDeep: Non-coding RNA classification with convolutional neural network

XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

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