Identifying protein arginine methylation sites using global features of protein sequence coupled with support vector machine optimized by particle swarm optimization algorithm

Enzymes play an important role in metabolism that helps in catalyzing bio-chemical reactions. Predicting functions of enzymes by experiments is costly and time consuming. Hence a computational method is required to predict the function of enzymes. This paper presents a supervised machine learning approach to predict the functional classes and subclass of protein sequences including enzymes and non-enzymes based on 857 sequence derived features. This paper used seven sequence derived properties including amino acid composition, dipeptide composition, correlation feature, composition, transition, distribution and pseudo amino acid composition. We have used recursive feature elimination technique (RFE), in order to select optimal number of features. The support vector machine (SVM) has been used to construct a three level model with optimal number of features selected by SVM-RFE, where top (first) level distinguish a query protein as an enzyme or nonenzyme, the next (second) level predicts the enzyme functional class and the last (third) level predict the subfunctional class. The proposed model reported overall accuracy of 97.6%, precision of 97.8%and Matthew Correlation Coefficient (MCC) value of 0.93 for the first level, whereas accuracy of 87.3%, precision of 87.7% and MCC value of 0.84 for second level and accuracy of 85.6%, precision of 87.9% and MCC value of 0.86 for the third level.

Anemia is one of the common adverse reactions after hip fracture surgery. The traditional method to solve anemia is allogeneic transfusion. However, the transfusion may lead to some complications such as septicemia and fever. So far, few studies have reported roles of machine learning in predicting whether blood transfusion is needed or not after hip fracture surgery. Therefore, the purpose of this study is to develop machine learning models to predict the likelihood of postoperative blood transfusion in patients undergoing hip fracture surgery. This study enrolled 1355 patients who underwent hip fracture surgery at the Affiliated Hospital of Qingdao University from January 2016 to December 2021. Among all patients, 210 cases received postoperative blood transfusion. All patients were randomly divided into a training group and a testing group at a ratio of 7:3. In the training group, univariate and multivariate logistic regression analyses were used to determine independent risk factors for the postoperative transfusion. Then, based on these independent risk factors, tenfold cross-validation method was utilized to develop five machine learning models, including logistic, multilayer perceptron (MLP), extreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM). The receiver operating characteristic (ROC) curve, area under ROC curve (AUC), and Matthews correlation coefficient (MCC) were generated to evaluate the performance of the models. Calibration plot and decision curve analysis (DCA) were used to test the performance, stability, and clinical applicability of the models. The models were validated using the testing group; and the ROC curve, MCC, calibration plot, and DCA curves were also generated to validate the performance, stability, and clinical applicability of the models. To further verify the robustness of the model, we randomly grabbed 70% of the samples in the testing set, performed 1000 iterations, and calculated the AUC and confidence interval of the five models. Finally, we used SHapley Additive exPlanations (SHAP) to explain these models. Multivariate logistic regression analysis showed that there were 8 independent risk factors, including age, blood transfusion history, albumin (ALB), globulin (GLO), total bilirubin (TBIL), indirect bilirubin (IBIL), hemoglobin (HB), and blood loss > 200ml. We finally selected five independent risk factors including HB, GLO, age, IBIL, and blood loss > 200ml. Based on these five independent risk factors, we generated six characteristic variables, namely HB, HB × HB, HB × blood loss, GLO × HB, age, age × IBIL, and established five machine learning models using a tenfold cross-validation method. In the training group, the AUC values of logistic, RF, MLP, SVM, and XGB were 0.9320, 0.8911, 0.9327, 0.9225, and 0.8825, respectively, and the average AUC was 0.9122 ± 0.0212. The MCC values were 0.65, 0.77, 0.65, 0.66, and 0.68, respectively, and the calibration plot and DCA performed well. In the testing group the AUC values of logistic, RF, MLP, SVM, and XGB were 0.8483, 0.7978, 0.8576, 0.8598, and 0.8216, respectively. The average AUC was 0.8370 ± 0.0238, and the MCC values were 0.41, 0.35, 0.40, 0.41, and 0.41, respectively. The calibration plot and DCA in the testing group also showed good performance. The AUC values and confidence intervals of the 1000-iteration model were: logistic (AUC, min confidence interval [CI]-max confidence interval [CI] 0.848, 0.804-0.903), RF (AUC, minCI-maxCI 0.797, 0.734-0.857), MLP (AUC, minCI-maxCI 0.858, 0.812-0.902), SVM (AUC, minCI-maxCI 0.859, 0.819-0.910), and XGB (AUC, minCI-maxCI 0.821, 0.764-0.894). The model performed well. Finally, according to SHAP, among all five models, HB played the most important role in model prediction and interpretation. The five models we developed all performed well in predicting the likelihood of blood transfusion after hip fracture surgery. Therefore, we believed that the prediction model based on machine learning had great application prospects in clinical practice, which could help clinicians better predict the risk of blood transfusion after hip fracture surgery.

The two transducers in the phototaxis system of the archaeon Halobacterium salinarum, HtrI and HtrII, are methyl-accepting proteins homologous to the chemotaxis transducers in eubacteria. Consensus sequences predict three glutamate pairs containing potential methylation sites in HtrI and one in HtrII. Mutagenic substitution of an alanine pair for one of these, Glu265-Glu266, in HtrI and for the homologous Glu513-Glu514 in HtrII eliminated methylation of these two transducers, as demonstrated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis autofluorography. Photostimulation of the repellent receptor sensory rhodopsin II (SRII) induced reversible demethylation of HtrII, while no detectable change in the extent of methylation of HtrI was observed in response to stimulation of its cognate sensory rhodopsin, the attractant receptor SRI. Cells containing HtrI or HtrII with all consensus sites replaced by alanine still exhibited phototaxis responses and behavioral adaptation, and methanol release assays showed that methyl group turnover was still induced in response to photostimulation of SRI or SRII. By pulse-chase experiments with in vivo L-[methyl-(3)H]methionine-labeled cells, we found that repetitive photostimulation of SRI complexed with wild-type (or nonmethylatable) HtrI induced methyl group turnover in transducers other than HtrI to the same extent as in wild-type HtrI. Both attractant and repellent stimuli cause a transient increase in the turnover rate of methyl groups in wild-type H. salinarum cells. This result is unlike that obtained with Escherichia coli, in which attractant stimuli decrease and repellent stimuli increase turnover rate, and is similar to that obtained with Bacillus subtilis, which also shows turnover rate increases regardless of the nature of the stimulus. We found that a CheY deletion mutant of H. salinarum exhibited the E. coli-like asymmetric pattern, as has recently also been observed in B. subtilis. Further, we demonstrate that the CheY-dependent feedback effect does not require the stimulated transducer to be methylatable and operates globally on other transducers present in the cell.

Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil

As peer-to-peer (P2P) technology booms lots of problems arise such as rampant piracy, congestion, low quality etc. Thus, accurate identification of P2P traffic makes great sense for efficient network management. As one of the optimal classifiers, support vector machine (SVM) has been successfully used in P2P traffic identification. However, the performance of SVM is largely dependent on its parameters and the traditional tuning methods are inefficient. In the paper, a novel hybrid method to optimize parameters of SVM based on cuckoo search algorithm combined with particle swarm optimization algorithm is proposed. The first stage of the proposed approach is to tune the best parameters for SVM with training data. Subsequently, the SVM configured with the best parameters is employed to identify P2P traffic. In the end, we demonstrate the effectiveness of our approach on-campus traffic traces. Experimental results indicate that the proposed method outperforms SVM based on genetic algorithm, particle swarm optimization algorithm and cuckoo search algorithm.

To resolve the problem of wetland remote sensing image classification, this paper presents an improved classification algorithm. In this algorithm, genetic algorithm (GA) selection, crossover operation is introduced to the standard particle swarm optimization algorithm (PSO) to form a hybrid particle swarm optimization algorithm (GAPSO). The hybrid algorithm can exploit the advantages of the genetic algorithm and particle swarm algorithm respectively to the full to obtain the global optimal parameters of support vector machine (SVM). Thus the wetland remote sensing image can be classified more accurately. Taking Ningxia Shahu wetland remote sensing images as an example, this paper makes a classification of wetland remote sensing images using optimized support vector machine, and the outcome of the experiment shows that this algorithm has better classification effect than that of similar algorithms. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.4325

Phospholipases, as important lipolytic enzymes, have diverse industrial applications. Regarding the stability of extremophilic archaea's proteins in harsh conditions, analyses of unusual features of their proteins are significantly important for their utilization. This research was accomplished to in silico study of archaeal phospholipases' properties and to develop a pioneering method for distinguishing these enzymes from other archaeal enzymes via machine learning algorithms and Chou's pseudo-amino acid composition concept. The non-redundant sequences of archaeal phospholipases were collected. BioSeq-Analysis sever was used with Support Vector Machine (SVM), Random Forests (RF), Covariance Discrimination (CD), and Optimized Evidence-Theoretic K-nearest Neighbor (OET-KNN) as powerful machine learnings algorithms. Also, different Chou's pseudo-amino acid composition modes were performed and then, 5-fold cross-validation was applied to the sequences. Based on our results, the OET-KNN predictor, with 96% accuracy, yields the best performance in SC-PseAAC mode by 5-fold cross-validation. This predictor also achieved very high values of specificity (95%), sensitivity (96%), Matthews's correlation coefficient (0.92), and accuracy (96%). The present investigation yielded a robust anticipatory model for the archaeal phospholipase prediction utilizing the tenets PseAAC and OET-KNN machine learning algorithm.

It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS) is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy) value of 79.72% and MCC (Matthew's correlation coefficient) value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance) strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier's vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

Brain tumors are abnormal tissue that grow uncontrolled and affect a patient's neurological function. Brain tumors come in different shapes and characteristics. Moreover, its location also differs for each patient. Brain tumors can be detected using machine learning algorithms using magnetic resonance imaging (MRI) images. However, a different machine-learning comparison is limited and needs further investigation. This study aims to compare three machine-learning methods, i.e., Support Vector Machine (SVM), Naive Bayes (NB), and Artificial Neural Network Back Propagation (ANN-BP) algorithms for detecting brain tumors. Before the comparison started, MRI image quality was enhanced by performing denoising, histogram equalization, and thresholding. After that, Gray Level Co-occurrence Matrix feature extraction was performed. MRI brain images in JPEG format were acquired from an open-access database. One thousand brain tumor and 1000 normal tumor images are used as the training data, while 100 brain tumor and 100 normal tumor images are used as testing data. Each algorithm's accuracy, precision, sensitivity, and Matthews Correlation Coefficient (MCC) are evaluated and reported. The study showed that the SVM algorithm acquired the highest performance in detecting brain tumors, followed by ANN-BP and NB. The highest accuracy, precision, sensitivity, and MCC values for testing in SVM were 98,75%, 98,22%, 99,30%, and 0,9751, respectively. Meanwhile, in testing, the highest accuracy, precision, sensitivity, and MCC values were 90.50%, 98.80%, 82.00%, and 0.8220, respectively. In conclusion, this study showed the superiority of the SVM algorithm in detecting brain tumor compared to ANN-BP and NB by performing image enhancement steps and GLCM feature extraction before its detection.

Small insertions/deletions (INDELs) of ≤21 bp comprise 18% of all recorded mutations causing human inherited disease and are evident in 24% of documented Mendelian diseases. INDELs affect gene function in multiple ways: for example, by introducing premature stop codons that either lead to the production of truncated proteins or affect transcriptional efficiency. However, the means by which they impact post-transcriptional regulation, including alternative splicing, have not been fully evaluated. In this study, we collate disease-causing INDELs from the Human Gene Mutation Database (HGMD) and neutral INDELs from the 1000 Genomes Project. The potential of these two types of INDELs to affect binding-site affinity of RNA-binding proteins (RBPs) was then evaluated. We identified several sequence features that can distinguish disease-causing INDELs from neutral INDELs. Moreover, we built a machine-learning predictor called PinPor (predicting pathogenic small insertions and deletions affecting post-transcriptional regulation, http://watson.compbio.iupui.edu/pinpor/) to ascertain which newly observed INDELs are likely to be pathogenic. Our results show that disease-causing INDELs are more likely to ablate RBP-binding sites and tend to affect more RBP-binding sites than neutral INDELs. Additionally, disease-causing INDELs give rise to greater deviations in binding affinity than neutral INDELs. We also demonstrated that disease-causing INDELs may be distinguished from neutral INDELs by several sequence features, such as their proximity to splice sites and their potential effects on RNA secondary structure. This predictor showed satisfactory performance in identifying numerous pathogenic INDELs, with a Matthews correlation coefficient (MCC) value of 0.51 and an accuracy of 0.75.

Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.

Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier, the parameters setting is very crucial to the learning results and generalization ability of SVM, and many irrelevant and redundant features degrade the performance of classification, furthermore, the choice of SVM parameters is influenced by the feature subset taken into account and vice versa. So a joint optimization method for selecting features and SVM parameters is proposed. Genetic algorithms, which have a lot of improvements about fitness, crossover and mutation in comparison with simple genetic algorithms, is to achieve a balance between the classification accuracy and the size of the feature subsets selected and have a fast convergence rate. Furthermore, ‘one-against-one’ approach is used to solve multi-class classification problems and a comparison of the model performances is made among the results of the joint optimization and the single optimization of features or SVM parameters in the experiments. The simulation result shows that our approach can find subsets of features for 4-class skin symptoms classification with higher average cross validation accuracy and a higher rate of convergence compared with single optimization of features or SVM parameters.

In this paper, a mode decomposition analysis-based adaptive approach is proposed to provide high diagnostic performance for automated voice pathology detection systems. The aim of the study is to develop a reliable and effective system using adaptive cepstral domain features derived from the empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete empirical mode decomposition with adaptive noise (CEEMDAN) methods. The descriptive feature sets are obtained by applying mel-frequency cepstral coefficients (MFCCs) and their derivatives, linear predictive coefficients (LPCs) and linear predictive cepstral coefficients (LPCCs) techniques to each decomposition level. The class-balanced data are generated on the VOice ICar fEDerico II database samples using the synthetic minority oversampling technique (SMOTE). The ReliefF algorithm is used to select the most effective and distinctive features. A combination of selected features and a support vector machine (SVM) classifier is used to identify pathological voices. In the pathology detection approach, the results show that the cepstral features based on EMD and SVM-cubic achieves the highest performance with 99.85\% accuracy, 99.85\% F1-score and 0.997 Matthews correlation coefficient (MCC). In pathology-type classification, the cepstral features based on EEMD and SVM-quadratic approach provided the highest performance with 96.49\% accuracy, 96.46\% F1 and 0.949 MCC values. The comprehensive results of this study reveal that mode decomposition-based approaches are more successful and effective than traditional methods for detection and classification of pathological voices.

Prediction subcellular localization of Gram-negative bacterial proteins by support vector machine using wavelet denoising and Chou's pseudo amino acid composition

S-Palmitoylation is a key regulatory mechanism controlling protein targeting, localization, stability, and activity. Since increasing evidence shows that its disruption is implicated in many human diseases, the identification of palmitoylation sites is attracting more attention. However, the computational methods that are published so far for this purpose have suffered from a poor balance of sensitivity and specificity; hence, it is difficult to get a good generalized prediction ability on an external validation set, which holds back the further analysis of associations between disruption of palmitoylation and human inherited diseases. In this work, we present a reliable identification method for protein S-palmitoylation sites, called SeqPalm, based on a series of newly composed features from protein sequences and the synthetic minority oversampling technique. With only 16 extracted key features, this approach achieves the most favorable prediction performance up to now with sensitivity, specificity, and Matthew's correlation coefficient values of 95.4%, 96.3%, and 0.917, respectively. Then, all known disease-associated variations are studied by SeqPalm. It is found that 243 potential loss or gain of palmitoylation sites are highly associated with human inherited disease. The analysis presents several potential therapeutic targets for inherited diseases associated with loss or gain of palmitoylation function. There are even biological evidence that are coordinate with our prediction results. Therefore, this work presents a novel approach to discover the molecular basis of pathogenesis associated with abnormal palmitoylation. SeqPalm is now available online at http://lishuyan.lzu.edu.cn/seqpalm , which can not only annotate the palmitoylation sites of proteins but also distinguish loss or gain of palmitoylation sites by protein variations.