Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Chen, Wei; Xing, Pengwei; Zou, Quan

doi:10.1038/srep40242

Cited by 110 publications

(57 citation statements)

References 31 publications

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“…Sensitivity (Sn), specificity (Sp), Accuracy (Acc) and Mathew’s correlation coefficient are the most common quantitative metrics used to gauge the performance of a predictor [ 27 , 28 , 29 , 31 , 32 , 33 , 34 ]. The following equations demonstrate how these metrics are computed using the results of self-consistency test The benchmark dataset collected contained a comparable number of positives and negatives.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

Prediction of N-linked glycosylation sites using position relative features and statistical moments

2017

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Glycosylation is one of the most complex post translation modification in eukaryotic cells. Almost 50% of the human proteome is glycosylated as glycosylation plays a vital role in various biological functions such as antigen’s recognition, cell-cell communication, expression of genes and protein folding. It is a significant challenge to identify glycosylation sites in protein sequences as experimental methods are time taking and expensive. A reliable computational method is desirable for the identification of glycosylation sites. In this study, a comprehensive technique for the identification of N-linked glycosylation sites has been proposed using machine learning. The proposed predictor was trained using an up-to-date dataset through back propagation algorithm for multilayer neural network. The results of ten-fold cross-validation and other performance measures such as accuracy, sensitivity, specificity and Mathew’s correlation coefficient inferred that the accuracy of proposed tool is far better than the existing systems such as Glyomine, GlycoEP, Ensemble SVM and GPP.

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Section: Experimentation and Resultsmentioning

confidence: 99%

Prediction of N-linked glycosylation sites using position relative features and statistical moments

2017

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“…If the value of j is too small, although the prediction accuracy is high, the time cost will be large. In general, when the j is in (5,10), the predicted fitness can be accurate while it can save optimization time. If the prediction model accuracy is lower than threshold, the prediction model needs to be updated, and then it continues iterating until the particles satisfy the demand.…”

Section: Self-renewing Fitness Prediction Methods Based On Pso Algoritmentioning

confidence: 99%

“…In recent years, the machine-learning method has greatly saved the time when optimizing design MSAs after being applied in the field of electromagnetism [2][3][4]. At present, the most commonly used methods are artificial neural network (ANN) [5][6][7][8], support vector machine (SVM) [9,10], and Gaussian process (GP) [11]. ese methods replace the true fitness calculation by constructing a prediction model, which shortens the time in entire optimization process by reducing the number of evaluations of the fitness calculation.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Multiband Microstrip Antennas by Self-Renewing Fitness Estimation of Particle Swarm Optimization Algorithm

Fan

Tian

Zhao

2019

International Journal of Antennas and Propagation

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In order to reduce the time of designing microstrip antenna, this paper proposes a self-renewing fitness estimation of particle swarm optimization algorithm (SFEPSO) to improve the design efficiency. Firstly, a fitness predictive model of the particles is constructed according to the evolution formula of particle swarm optimization (PSO). From the third generation of the algorithm, the fitness of particles is given by the prediction model instead of the full-wave electromagnetic simulation. Aiming to keep the right direction of evolution, the prediction model will be proofed every N generation during the optimization process. If the prediction model accuracy is lower than the threshold, it will be updated and then continue iterating until the particles satisfy the demands. Compared with the traditional optimization method, this method greatly reduces the evaluation time and improves the design efficiency. e method is validated by the optimized design of E-type dual-frequency microstrip antenna and WLAN/ WiMAX multiband antenna. e optimized results show that the purpose of rapid optimization can be achieved while ensuring the design accuracy.

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“…Support vector machine is a popular classifier which has solved several bioinformatics problems (Li et al, 2016;Chen et al, 2017;Bu et al, 2018;Zhang et al, 2018;Chao et al, 2019a,b;Sun et al, 2019;Wang et al, 2019). The "caret" R package was used to train models and tune the model hyperparameters based on SVM (Kuhn, 2008).…”

Section: Model Training and Evaluationmentioning

confidence: 99%

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Sun

Ding

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

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Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines

Cited by 110 publications

References 31 publications

Prediction of N-linked glycosylation sites using position relative features and statistical moments

Prediction of N-linked glycosylation sites using position relative features and statistical moments

Optimal Design of Multiband Microstrip Antennas by Self-Renewing Fitness Estimation of Particle Swarm Optimization Algorithm

Identifying Antifreeze Proteins Based on Key Evolutionary Information

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