Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Dao, Fanny; Yang, Hui; Su, Zhixun; Yang, Wen-Lin; Wu, Yun; Ding, Hui; Chen, Wei; Tang, Hua; Lin, Hao

doi:10.3390/molecules22071057

Cited by 54 publications

(38 citation statements)

References 107 publications

(182 reference statements)

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“…There are three kinds of cross-validation methods: the n-fold cross-validation, the jackknife cross-validation, and the independent data test [ 15 ]. Among the three tests, the jackknife test has been widely used in bioinformatics because it could produce a unique outcome [ 16 , 17 , 18 , 19 , 20 ].…”

Section: Resultsmentioning

confidence: 99%

A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

Long

Liao

et al. 2018

IJMS

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Protein hydroxylation is one type of post-translational modifications (PTMs) playing critical roles in human diseases. It is known that protein sequence contains many uncharacterized residues of proline and lysine. The question that needs to be answered is: which residue can be hydroxylated, and which one cannot. The answer will not only help understand the mechanism of hydroxylation but can also benefit the development of new drugs. In this paper, we proposed a novel approach for predicting hydroxylation using a hybrid deep learning model integrating the convolutional neural network (CNN) and long short-term memory network (LSTM). We employed a pseudo amino acid composition (PseAAC) method to construct valid benchmark datasets based on a sliding window strategy and used the position-specific scoring matrix (PSSM) to represent samples as inputs to the deep learning model. In addition, we compared our method with popular predictors including CNN, iHyd-PseAAC, and iHyd-PseCp. The results for 5-fold cross-validations all demonstrated that our method significantly outperforms the other methods in prediction accuracy.

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Section: Resultsmentioning

confidence: 99%

A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

Long

Liao

et al. 2018

IJMS

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“…SVM is a supervised learning model for pattern recognition, classification, and regression analysis. It has been successfully applied in the field of bioinformatics (Cao et al, 2014;Chen et al, 2016d; 2016; Yang et al, 2016;Zhao et al, 2016Zhao et al, , 2017Zou et al, 2016a;Dao et al, 2017;Lai et al, 2017;Lin et al, 2017;Manavalan and Lee, 2017;Manavalan et al, , 2018aManavalan et al, , 2018bFeng et al, 2018). The basic idea of SVM is to transform the data into a high-dimensional feature space and then determine the optimal separating hyperplane.…”

Section: Support Vector Machinementioning

confidence: 99%

iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites inHomo sapiens

Yang

Ding

et al. 2018

Journal of Computational Biology

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129

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2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2'-O-methylation sites. In this study, based on the experimental 2'-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2'-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%.

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“…Cross-validation is a popular strategy for assessing how the results of statistical analysis techniques generalize to an independent data set [47]. To select an accurate model, the researchers developed several cross validation techniques, including the k-fold cross-validation, jackknife cross-validation, and independent data test [48][49][50][51][52][53]. This research work, employed the k-fold cross validation technique to select the appropriate machine learning predictor.…”

Section: Cross Validationmentioning

confidence: 99%

Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

2018

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Precise information of fault location plays a vital role in expediting the restoration process, after being subjected to any kind of fault in power distribution grids. This paper proposed the Stockwell transform (ST) based optimized machine learning approach, to locate the faults and to identify the faulty sections in the distribution grids. This research employed the ST to extract useful features from the recorded three-phase current signals and fetches them as inputs to different machine learning tools (MLT), including the multilayer perceptron neural networks (MLP-NN), support vector machines (SVM), and extreme learning machines (ELM). The proposed approach employed the constriction-factor particle swarm optimization (CF-PSO) technique, to optimize the parameters of the SVM and ELM for their better generalization performance. Hence, it compared the obtained results of the test datasets in terms of the selected statistical performance indices, including the root mean squared error (RMSE), mean absolute percentage error (MAPE), percent bias (PBIAS), RMSE-observations to standard deviation ratio (RSR), coefficient of determination (R2), Willmott’s index of agreement (WIA), and Nash–Sutcliffe model efficiency coefficient (NSEC) to confirm the effectiveness of the developed fault location scheme. The satisfactory values of the statistical performance indices, indicated the superiority of the optimized machine learning tools over the non-optimized tools in locating faults. In addition, this research confirmed the efficacy of the faulty section identification scheme based on overall accuracy. Furthermore, the presented results validated the robustness of the developed approach against the measurement noise and uncertainties associated with pre-fault loading condition, fault resistance, and inception angle.

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Recent Advances in Conotoxin Classification by Using Machine Learning Methods

Cited by 54 publications

References 107 publications

A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

A Hybrid Deep Learning Model for Predicting Protein Hydroxylation Sites

iRNA-2OM: A Sequence-Based Predictor for Identifying 2′-O-Methylation Sites inHomo sapiens

Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach

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