Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection

Ju, Zhe; He, Jianjun

doi:10.1016/j.ab.2018.04.005

Cited by 35 publications

(40 citation statements)

References 34 publications

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“…To further demonstrate the effectiveness of the proposed model, the independent test dataset was used to compare the model with existing prediction tool. Considering previously published prediction tools, only one predictor of glutarylation sites, GlutPred [55], was freely available. We compared our predictive performance to that of GlutPred based on independent testing datasets.…”

Section: Discussionmentioning

confidence: 99%

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

et al. 2019

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Background: Glutarylation, the addition of a glutaryl group (five carbons) to a lysine residue of a protein molecule, is an important post-translational modification and plays a regulatory role in a variety of physiological and biological processes. As the number of experimentally identified glutarylated peptides increases, it becomes imperative to investigate substrate motifs to enhance the study of protein glutarylation. We carried out a bioinformatics investigation of glutarylation sites based on amino acid composition using a public database containing information on 430 non-homologous glutarylation sites. Results: The TwoSampleLogo analysis indicates that positively charged and polar amino acids surrounding glutarylated sites may be associated with the specificity in substrate site of protein glutarylation. Additionally, the chi-squared test was utilized to explore the intrinsic interdependence between two positions around glutarylation sites. Further, maximal dependence decomposition (MDD), which consists of partitioning a large-scale dataset into subgroups with statistically significant amino acid conservation, was used to capture motif signatures of glutarylation sites. We considered single features, such as amino acid composition (AAC), amino acid pair composition (AAPC), and composition of k-spaced amino acid pairs (CKSAAP), as well as the effectiveness of incorporating MDD-identified substrate motifs into an integrated prediction model. Evaluation by five-fold cross-validation showed that AAC was most effective in discriminating between glutarylation and non-glutarylation sites, according to support vector machine (SVM). Conclusions: The SVM model integrating MDD-identified substrate motifs performed well, with a sensitivity of 0.677, a specificity of 0.619, an accuracy of 0.638, and a Matthews Correlation Coefficient (MCC) value of 0.28. Using an independent testing dataset (46 glutarylated and 92 non-glutarylated sites) obtained from the literature, we demonstrated that the integrated SVM model could improve the predictive performance effectively, yielding a balanced sensitivity and specificity of 0.652 and 0.739, respectively. This integrated SVM model has been implemented as a web-based system (MDDGlutar), which is now freely available at http://csb.cse.yzu. edu.tw/MDDGlutar/.

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Section: Discussionmentioning

confidence: 99%

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

et al. 2019

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“…To the best of our knowledge, we have identified three Glutarylation site predictors with the most promising results. In 2018, GlutPred [22] was developed using multiple feature extraction techniques along with maximum relevance and minimum redundancy feature selections to predict the Glutarylation sites. In the same year, iGlu-Lys [23] was developed using the finest features to predict the Glutarylation sites from the four-encodings method.…”

Section: Comparison With State-of-the-art Modelsmentioning

confidence: 99%

“…During the past few years, a wide range of methods has been proposed to predict Glutarylation sites using many machine learning approaches [20][21][22][23][24][25]. Recently, many deep learning models have been used to predict different types of PTMs [6,[26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…In a different study, López et al proposed structural features and evolutionary information of amino acids to predict the succinylation sites, which is closely related to Glutarylation sites prediction [20,21]. Recently, Zhe et al [22] developed a predictor tool named GlutPred. To predict the Glutarylation sites, they extracted different kinds of features and applied a maximum relevance minimum redundancy feature selection method.…”

Section: Introductionmentioning

confidence: 99%

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Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features

Arafat

Ahmad

Shovan

et al. 2020

Genes

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Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew’s Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.

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“…The method of mRMR uses the correlation between the information as an evaluation index, which can then find the optimal characteristic subset. 15 Assuming that an m-dimensional fault sample space with c fault characteristics and n fault samples, the mRMR can select an optimal subsample space S with p-dimension ðp mÞ from the original sample space. In the subsample space S, the redundancy of repeated information between each fault feature is the smallest, and the correlation between the features and the faults is the largest.…”

Section: Feature Selection Based On Mrmrmentioning

confidence: 99%

Stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

Kang

Feng

Wan

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Cavitation is a phenomenon that occurs during the continuous operation of a hydro turbine that directly affects the efficiency and working capacity of the unit. This paper proposes an innovative classification paradigm that uses deep learning-based methodologies in order to identify both cavitation noise signal and non-cavitation noise signal that will help prevent the damage or breakage in the earliest possible time to avoid more irreversible and irreparable damage to the hydro turbine. The stacked sparse autoencoder (SSA) nuclear framework is utilized to learn more abstract and invariant high-level features from the multiple feature sets. Then, the method on minimum redundancy maximum relevance (mRMR) selection is used to evaluate and sort out all the characteristics found by the stacked sparse autoencoder. Finally, the random forest (RF) classifier algorithm is employed to perform supervised fine-tuning and classification. The traditional supervised learning models such as support vector machine, logistic regression, and sparse representation classification are chosen to be used as contrast algorithms. SSA-mRMR-RF generally produces a better performance than the support vector machine, logistic regression and sparse representation classification when used in the same set of features. The SSA-mRMR-RF produced the highest overall average accuracy since it reached 93.18%. The SSA-mRMR-RF offers a 3.85% higher overall accuracy than the support vector machine. It also offers a 2.44% higher overall accuracy than logistic regression. In addition, it also offers a 20.30% higher overall accuracy than the sparse representation classification on the average. When the signals are divided into three categories, the highest overall accuracy decreased to 71.88%, and the classification accuracy of incipient noise signals is very low. Therefore, this paper proposes the models of power spectral-SSA-mRMR-RF and fast Fourier transform-SSA-mRMR-RF to be used, and these discoveries are based on the frequency characteristics of the cavitation noise signal. The highest overall accuracy rebound went back to 88.97% based on the power spectrum, and 96.05% based on fast Fourier transform. The corresponding experimental results found on the four data sets of the different operating conditions demonstrate that the proposed methods for discovering the extent of cavitation outperforms these traditional alternatives.

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Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection

Cited by 35 publications

References 34 publications

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

Characterization and identification of lysine glutarylation based on intrinsic interdependence between positions in the substrate sites

Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features

Stacked sparse autoencoder in cavitation noise signal data classification of hydro turbine based on power spectrum

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