Identification of Phage Virion Proteins by Using the g-gap Tripeptide Composition

2019 ◽  
Vol 16 (4) ◽  
pp. 332-339
Author(s):  
Liangwei Yang ◽  
Hui Gao ◽  
Zhen Liu ◽  
Lixia Tang
Keyword(s):  
Information Gain ◽  
State Of The Art ◽  
Support Vector ◽  
Feature Subset ◽  
Bacterial Cells ◽  
Accurate Identification ◽  
Incremental Feature Selection ◽  
Optimal Feature Subset ◽  
Phage Proteins ◽  
Optimal Feature

Phages are widely distributed in locations populated by bacterial hosts. Phage proteins can be divided into two main categories, that is, virion and non-virion proteins with different functions. In practice, people mainly use phage virion proteins to clarify the lysis mechanism of bacterial cells and develop new antibacterial drugs. Accurate identification of phage virion proteins is therefore essential to understanding the phage lysis mechanism. Although some computational methods have been focused on identifying virion proteins, the result is not satisfying which gives more room for improvement. In this study, a new sequence-based method was proposed to identify phage virion proteins using g-gap tripeptide composition. In this approach, the protein features were firstly extracted from the ggap tripeptide composition. Subsequently, we obtained an optimal feature subset by performing incremental feature selection (IFS) with information gain. Finally, the support vector machine (SVM) was used as the classifier to discriminate virion proteins from non-virion proteins. In 10-fold crossvalidation test, our proposed method achieved an accuracy of 97.40% with AUC of 0.9958, which outperforms state-of-the-art methods. The result reveals that our proposed method could be a promising method in the work of phage virion proteins identification.

scholarly journals Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8370
Author(s):  
Ala Hag ◽  
Dini Handayani ◽  
Maryam Altalhi ◽  
Thulasyammal Pillai ◽  
Teddy Mantoro ◽  
...  
Keyword(s):  
Feature Selection ◽  
Vector Space ◽  
Frequency Domain ◽  
Mental Stress ◽  
State Of The Art ◽  
The State ◽  
Support Vector ◽  
Feature Subset ◽  
Optimal Feature Subset ◽  
Optimal Feature

In real-life applications, electroencephalogram (EEG) signals for mental stress recognition require a conventional wearable device. This, in turn, requires an efficient number of EEG channels and an optimal feature set. This study aims to identify an optimal feature subset that can discriminate mental stress states while enhancing the overall classification performance. We extracted multi-domain features within the time domain, frequency domain, time-frequency domain, and network connectivity features to form a prominent feature vector space for stress. We then proposed a hybrid feature selection (FS) method using minimum redundancy maximum relevance with particle swarm optimization and support vector machines (mRMR-PSO-SVM) to select the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art metaheuristic methods. The proposed model significantly reduced the features vector space by an average of 70% compared with the state-of-the-art methods while significantly increasing overall detection performance.

scholarly journals A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi Chen ◽  
Tao Lin ◽  
Ningjiu Tang ◽  
Xin Xia
Keyword(s):  
Genetic Algorithm ◽  
Classification Accuracy ◽  
Coarse Grained ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Subset ◽  
Parallel Genetic Algorithm ◽  
Support Vectors ◽  
Optimal Feature Subset ◽  
Optimal Feature

The extensive applications of support vector machines (SVMs) require efficient method of constructing a SVM classifier with high classification ability. The performance of SVM crucially depends on whether optimal feature subset and parameter of SVM can be efficiently obtained. In this paper, a coarse-grained parallel genetic algorithm (CGPGA) is used to simultaneously optimize the feature subset and parameters for SVM. The distributed topology and migration policy of CGPGA can help find optimal feature subset and parameters for SVM in significantly shorter time, so as to increase the quality of solution found. In addition, a new fitness function, which combines the classification accuracy obtained from bootstrap method, the number of chosen features, and the number of support vectors, is proposed to lead the search of CGPGA to the direction of optimal generalization error. Experiment results on 12 benchmark datasets show that our proposed approach outperforms genetic algorithm (GA) based method and grid search method in terms of classification accuracy, number of chosen features, number of support vectors, and running time.

scholarly journals Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

2020 ◽  
Vol 12 (11) ◽  
pp. 1903
Author(s):  
Cheng Hu ◽  
Shaoyang Kong ◽  
Rui Wang ◽  
Fan Zhang ◽  
Lianjun Wang
Keyword(s):  
Support Vector Regression ◽  
Cross Section ◽  
Radar Cross Section ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Support Vector ◽  
Feature Subset ◽  
Mass Estimation ◽  
Optimal Feature Subset ◽  
Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

Fault detection in mixture production process based on wavelet packet and support vector machine

2021 ◽  
pp. 1-15
Author(s):  
Yan Chen ◽  
Huan-sheng song ◽  
Yan-ni yang ◽  
Gang-feng wang
Keyword(s):  
Production Process ◽  
Wavelet Packet ◽  
Road Construction ◽  
Support Vector ◽  
Feature Subset ◽  
Svm Classification ◽  
De Algorithm ◽  
Optimal Feature Subset ◽  
Optimal Feature

Mixture production equipment is widely employed in road construction, and the quality of the produced mixture is the essential factor to ensure the quality of road construction. To detect the quality of the real-time produced mixture and solve the shortcomings of laboratory detection lag, a new fault detection method in the mixture production process is proposed, which is based on wavelet packet decomposition (WPD) and support vector machine (SVM). The proposed scheme includes feature extraction, feature selection, SVM classification, and optimization algorithm. During feature extraction, wavelet basis function is utilized to 4-layer decompose the aggregate and asphalt data mixed in real-time. The energy value calculated by wavelet packet coefficient is the extracted feature. During feature selection, a method combining the chi-square test and wrapper (CSW) is conducted to select the optimal feature subset from WPD features. Eventually, by adopting the optimal feature subset, SVM has been developed to classify various faults. Its parameters are optimized by differential evolution (DE) algorithm. In the test stage, multiple faults of different specifications of aggregates and asphalt are detected in the mixture production process. The results demonstrate that (1) accuracy produced by the CSW method with WPD features is 4.33% higher than the PCA method with statistical features; (2) SVM classification method optimized by DE algorithm brings an increase in recognition accuracy of identifying different types of mixture production faults produced by different equipment. Compared to other available methods, the proposed algorithm has a very outstanding detection performance.

scholarly journals m5CPred-SVM:  A Novel Method for Predicting m5C Sites of RNA

2020 ◽  
Author(s):  
Xiao Chen ◽  
Yi Xiong ◽  
Yinbo Liu ◽  
Yuqing Chen ◽  
Shoudong Bi ◽  
...  
Keyword(s):  
Cell Fate ◽  
Prediction Accuracy ◽  
Cytosine Methylation ◽  
Computational Method ◽  
Selection Strategy ◽  
Support Vector ◽  
Feature Subset ◽  
Accurate Identification ◽  
Benchmark Datasets ◽  
Optimal Feature Subset

Abstract Background: As one of the most common post-transcriptional modifications (PTCM) in RNA, 5-cytosine-methylation plays important roles in many biological functionssuch as RNA metabolism and cell fate decision. Through accurate identification of 5-methylcytosine (m5C) sites on RNA,researcherscanbetter understandthe exact role of 5-cytosine-methylation in these biological functions. In recent years, computational methods of predicting m5C sites have attracted lots of interests because of its efficiency and low-cost.However, both the accuracy and efficiency of these methods are not satisfactory yet and need further improvement.Results: In this work, we have developed a new computational method, m5CPred-SVM, to identify m5C sites in three species, H. sapiens, M. musculus and A. thaliana. To build this model, we first collected benchmark datasets following three recently published methods. Then, six types of sequence-based features were generated based on RNA segments and the sequential forward feature selection strategy was used to obtain the optimal feature subset. After that, the performance of models based on different learning algorithms were compared, and the model based on the support vector machine provided the highest prediction accuracy. Finally, our proposed method, m5CPred-SVM was compared with several existing methods, and the result showed that m5CPred-SVMoffered substantially higher prediction accuracy thanpreviously published methods. It is expected that our method, m5CPred-SVM, can become a useful tool for accurate identification of m5C sites.Conclusion: In this study, by introducing position-specific propensity related features, we built a new model, m5CPred-SVM, to predict RNA m5C sites of three different species.The result shows that our model outperformed the existing state-of-art models.Our model is available for users through a web serverat http://zhulab.ahu.edu.cn/m5CPred-SVM.

NeuroCS: A Tool to Predict Cleavage Sites of Neuropeptide Precursors

2020 ◽  
Vol 27 (4) ◽  
pp. 337-345 ◽  
Author(s):  
Ying Wang ◽  
Juanjuan Kang ◽  
Ning Li ◽  
Yuwei Zhou ◽  
Zhongjie Tang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Characteristic Curve ◽  
Training Dataset ◽  
Support Vector ◽  
Feature Subset ◽  
Cleavage Sites ◽  
Accurate Identification ◽  
Testing Dataset ◽  
Optimal Feature Subset ◽  
Independent Testing Dataset

Background: Neuropeptides are a class of bioactive peptides produced from neuropeptide precursors through a series of extremely complex processes, mediating neuronal regulations in many aspects. Accurate identification of cleavage sites of neuropeptide precursors is of great significance for the development of neuroscience and brain science. Objective: With the explosive growth of neuropeptide precursor data, it is pretty much needed to develop bioinformatics methods for predicting neuropeptide precursors’ cleavage sites quickly and efficiently. Method : We started with processing the neuropeptide precursor data from SwissProt and NueoPedia into two sets of data, training dataset and testing dataset. Subsequently, six feature extraction schemes were applied to generate different feature sets and then feature selection methods were used to find the optimal feature subset of each. Thereafter the support vector machine was utilized to build models for different feature types. Finally, the performance of models were evaluated with the independent testing dataset. Results: Six models are built through support vector machine. Among them the enhanced amino acid composition-based model reaches the highest accuracy of 91.60% in the 5-fold cross validation. When evaluated with independent testing dataset, it also showed an excellent performance with a high accuracy of 90.37% and Area under Receiver Operating Characteristic curve up to 0.9576. Conclusion: The performance of the developed model was decent. Moreover, for users’ convenience, an online web server called NeuroCS is built, which is freely available at http://i.uestc.edu.cn/NeuroCS/dist/index.html#/. NeuroCS can be used to predict neuropeptide precursors’ cleavage sites effectively.

Reseach on Feature Selection Algorithm Based on the Margin of Support Vector Machine

2013 ◽  
Vol 333-335 ◽  
pp. 1430-1434
Author(s):  
Lin Fang Hu ◽  
Lei Qiao ◽  
Min De Huang
Keyword(s):  
Feature Selection ◽  
Weather Forecast ◽  
Support Vector ◽  
Feature Subset ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Optimal Hyperplane ◽  
Optimal Feature Subset ◽  
Optimal Feature

A feature selection algorithm based on the optimal hyperplane of SVM is raised. Using the algorithm, the contribution to the classification of each feature in the candidate feature set is test, and then the feature subset with best classification ability will be selected. The algorithm is used in the recognition process of storm monomers in weather forecast, and experimental data show that the classification ability of the features can be effectively evaluated; the optimal feature subset is selected to enhance the working performance of the classifier.

scholarly journals Enhancing EEG-Based Mental Stress State Recognition using an Improved Hybrid Feature Selection Algorithm

2021 ◽  
Author(s):  
ALA HAG ◽  
Dini Handayani ◽  
Maryam Altalhi ◽  
Thulasyammal Pillai ◽  
Teddy Mantoro ◽  
...  
Keyword(s):  
Feature Selection ◽  
Stress State ◽  
Vector Space ◽  
Frequency Domain ◽  
Mental Stress ◽  
State Of The Art ◽  
Feature Subset ◽  
State Recognition ◽  
Optimal Feature Subset ◽  
Optimal Feature

Mental stress state recognition using electroencephalogram (EEG) signals for real-life applications needs a conventional wearable device. This requires an efficient number of EEG channels and an optimal feature set. The main objective of the study is to identify an optimal feature subset that can best discriminate mental stress states while enhancing the overall performance. Thus, multi-domain feature extraction methods were employed, namely, time domain, frequency domain, time-frequency domain, and network connectivity features, to form a large feature vector space. To avoid the computational complexity of high dimensional space, a hybrid feature selection (FS) method of minimum Redundancy Maximum Relevance with Particle Swarm Optimization and Support Vector Machine (mRMR-PSO-SVM) is proposed to remove noise, redundant, and irrelevant features and keep the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art heuristic methods. The proposed model has significantly reduced the features vector space by an average of 70% in comparison to the state-of-the-art methods while significantly increasing overall detection performance.

Data Classification Using Support Vector Machines with Mixture Kernels

2013 ◽  
Vol 662 ◽  
pp. 936-939 ◽  
Author(s):  
Li Wei Wei ◽  
Chuan Shen Wei ◽  
Xia Qing Wan
Keyword(s):  
Statistical Models ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feature Subset ◽  
Computational Costs ◽  
Learning Techniques ◽  
Single Feature ◽  
Vector Machines ◽  
Optimal Feature Subset ◽  
Optimal Feature

Recent studies have showed that machine learning techniques are advantageous to statistical models for medicine database classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. Three UCI databases are used to demonstrate the good performance of the SVM- MK.

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