2020
DOI: 10.1093/bib/bbaa128
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Using deep neural networks and biological subwords to detect protein S-sulfenylation sites

Abstract: Abstract Protein S-sulfenylation is one kind of crucial post-translational modifications (PTMs) in which the hydroxyl group covalently binds to the thiol of cysteine. Some recent studies have shown that this modification plays an important role in signaling transduction, transcriptional regulation and apoptosis. To date, the dynamic of sulfenic acids in proteins remains unclear because of its fleeting nature. Identifying S-sulfenylation sites, therefore, could be… Show more

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Cited by 66 publications
(44 citation statements)
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“…These data are presented as the percentage of the correct predictions on a different set of data such as positive, negative, and all data. These evaluation measurements are defined in previous works on different aspects of biomedical tasks [38,39] as follows:…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…These data are presented as the percentage of the correct predictions on a different set of data such as positive, negative, and all data. These evaluation measurements are defined in previous works on different aspects of biomedical tasks [38,39] as follows:…”
Section: Discussionmentioning
confidence: 99%
“…These data are presented as the percentage of the correct predictions on a different set of data such as positive, negative, and all data. These evaluation measurements are defined in previous works on different aspects of biomedical tasks [ 38 , 39 ] as follows: where TP, TN, FP, and FN denote true positives, true negatives, false positives, and false negatives, respectively. Moreover, to overcome the possibilities of the imbalance dataset, we reported the ROC curve and AUC values to observe the overall performance at different threshold points.…”
Section: Methodsmentioning
confidence: 99%
“…All of the aforementioned algorithms were executed by using Weka with 10-fold cross-validation. To evaluate the performance of all constructed models in both machine learning and deep learning methods, we used five measurements that were commonly used in binary classification tasks [ 35 , 36 ] (all data was encoded) including sensitivity, specificity, accuracy, Matthews correlation coefficient ( MCC ) and Area Under the Receiver Operating Curve (AUC). These evaluation measurements are defined as follows: where TP , TN , FP , and FN respectively denote true positives, true negatives, false positives, and false negatives.…”
Section: Methodsmentioning
confidence: 99%
“…Recently, deep learning has attracted attention in molecular ( Le et al, 2019a , b ; Do et al, 2020 ) and biomedical image analysis ( Wang J. et al, 2016 ; Jiang et al, 2020 ; Li et al, 2020 ). In biomedical image analysis, CNNs represents the mainstream approach.…”
Section: Introductionmentioning
confidence: 99%