Identification of S-nitrosylation sites based on multiple features combination

Li, Taoying; Song, Runyu; Yin, Qian; Gao, Mingyue; Chen, Yan

doi:10.1038/s41598-019-39743-9

Cited by 14 publications

(8 citation statements)

References 50 publications

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“…A binary-classification model is usually evaluated by the following variables and performance metrics. 65 , 85 , 86 ITP patients are defined as positive samples, and their number is defined as P. The controls are negative samples, and their number is N. ITP patients are true positives (TPs) if they are predicted as the ITP class; otherwise, they are the false negatives (FNs). The control samples are defined as true negatives (TNs) if they are correctly predicted as controls; otherwise, they are defined as false positives (FP).…”

Section: Methodsmentioning

confidence: 99%

A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

Xu¹,

Zhang²,

Duan³

et al. 2022

Molecular Therapy - Nucleic Acids

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Section: Methodsmentioning

confidence: 99%

A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

Xu¹,

Zhang²,

Duan³

et al. 2022

Molecular Therapy - Nucleic Acids

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“…In 2019, Li et al [ 70 ] predicted S-nitrosylation sites by multifeature fusion. In this study, they used 731 positive sites and 810 negative sites of iSNO-PseAAC and iSNO-AAPair as the training set and 43 positive sites and 121 negative sites of Li et al as the test set.…”

Section: Methodsmentioning

confidence: 99%

“…Soon, Li et al [ 70 ] proposed a method to predict protein S-nitrosylation sites using multifeature mixing. This work improves prediction performance by extracting nine sequence features, such as parallel correlation pseudoamino acid composition (PC-PseAAC), general parallel correlation pseudoamino acid composition [ 136 ], and ANBPB.…”

Section: Research Reviewmentioning

confidence: 99%

Recent Advances in Predicting Protein S-Nitrosylation Sites

Zhao

Xie

et al. 2021

BioMed Research International

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Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.

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“…The pooled features are presented to diverse numbers of classifiers and grouped into the anticipated classes. We utilize the SVM classification algorithm to define the ROI as normal, benign tumor, or cancer [36][37][38]. The proposed system is composed of three main phases: feature extraction, texture extraction, and tumor classification.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

Mahmoud¹,

Alharbi²,

Khafga³

2021

Intelligent Automation &Amp; Soft Computing

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In this paper, we aim to apply deep learning convolution neural network (Deep-CNN) technology to classify breast masses in mammograms. We develop a Deep-CNN combined with multi-feature extraction and transfer learning to detect breast cancer. The Deep-CNN is utilized to extract features from mammograms. A support vector machine (SVM) is then trained on the Deep-CNN features to classify normal, benign, and cancer cases. The scoring features from the Deep-CNN are coupled with texture features and used as inputs to the final classifier. Two texture features are included: texture features of spatial dependency and gradient-based histograms. Both are employed to locate breast masses in mammograms. Next we apply transfer learning to the classifier of the SVM. Four techniques are devised for the experimental evaluation of the proposed system. The fourth technique combines the Deep-CNN with texture features and local features extracted by the scale-invariant feature transform (SIFT) algorithm. Experiments are designed to measure the performance of the various techniques. The results demonstrate that the proposed CNN coupled with the texture features and the SIFT outperforms the other models and performs best with transfer learning embedded. The accuracy of this model is 97.8%, with a true positive rate of 98.45% and a true negative rate of 96%.

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Identification of S-nitrosylation sites based on multiple features combination

Cited by 14 publications

References 50 publications

A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

Recent Advances in Predicting Protein S-Nitrosylation Sites

Breast Cancer Classification Using Deep Convolution Neural Network with Transfer Learning

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