iPVP-MCV: A Multi-Classifier Voting Model for the Accurate Identification of Phage Virion Proteins

The classic structure of a bacteriophage is commonly characterized by complex symmetry. The head of the structure features icosahedral symmetry, whereas the tail features helical symmetry. The phage virion protein (PVP), a type of bacteriophage structural protein, is an essential material of the infectious viral particles and is responsible for multiple biological functions. Accurate identification of PVPs is of great significance for comprehending the interaction between phages and host bacteria and developing new antimicrobial drugs or antibiotics. However, traditional experimental approaches for identifying PVPs are often time-consuming and laborious. Therefore, the development of computational methods that can efficiently and accurately identify PVPs is desired. In this study, we proposed a multi-classifier voting model called iPVP-MCV to enhance the predictive performance of PVPs based on their amino acid sequences. First, three types of evolutionary features were extracted from the position-specific scoring matrix (PSSM) profiles to represent PVPs and non-PVPs. Then, a set of baseline models were trained based on the support vector machine (SVM) algorithm combined with each type of feature descriptors. Finally, the outputs of these baseline models were integrated to construct the proposed method iPVP-MCV by using the majority voting strategy. Our results demonstrated that the proposed iPVP-MCV model was superior to existing methods when performing the rigorous independent dataset test.

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iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

International Journal of Molecular Sciences ◽

10.3390/ijms22168958 ◽

2021 ◽

Vol 22 (16) ◽

pp. 8958

Author(s):

Phasit Charoenkwan ◽

Chanin Nantasenamat ◽

Md. Mehedi Hasan ◽

Mohammad Ali Moni ◽

Pietro Lio’ ◽

...

Keyword(s):

Machine Learning ◽

Large Scale ◽

De Novo ◽

Predictive Performance ◽

Support Vector ◽

Sufficient Information ◽

Self Assessment ◽

Accurate Identification ◽

Bitter Peptides ◽

Accurate Performance

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides

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Incorporating Amino Acids Composition and Functional Domains for Identifying Bacterial Toxin Proteins

BioMed Research International ◽

10.1155/2014/972692 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Min-Gang Su ◽

Chien-Hsun Huang ◽

Tzong-Yi Lee ◽

Yu-Ju Chen ◽

Hsin-Yi Wu

Keyword(s):

Amino Acids ◽

Cell Biology ◽

Predictive Performance ◽

Computational Prediction ◽

Amino Acid Sequences ◽

Bacterial Toxins ◽

Bacterial Toxin ◽

Support Vector ◽

Functional Domain ◽

Domain Information

Aside from pathogenesis, bacterial toxins also have been used for medical purpose such as drugs for cancer and immune diseases. Correctly identifying bacterial toxins and their types (endotoxins and exotoxins) has great impact on the cell biology study and therapy development. However, experimental methods for bacterial toxins identification are time-consuming and labor-intensive, implying an urgent need for computational prediction. Thus, we are motivated to develop a method for computational identification of bacterial toxins based on amino acid sequences and functional domain information. In this study, a nonredundant dataset of 167 bacterial toxins including 77 exotoxins and 90 endotoxins is adopted to learn the predictive model by using support vector machines (SVMs). The cross-validation evaluation shows that the SVM models trained with amino acids and dipeptides composition could yield an accuracy of 96.07% and 92.50%, respectively. For discriminating endotoxins from exotoxins, the SVM models trained with amino acids and dipeptides composition have achieved an accuracy of 95.71% and 92.86%, respectively. After incorporating functional domain information, the predictive performance is further improved. The proposed method has been demonstrated to be able to more effectively identify and classify bacterial toxins than the other two features on independent dataset, which may aid in bacterial biomedical development.

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Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/5770981 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Zhehan Shen ◽

Taigang Liu ◽

Ting Xu

Keyword(s):

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Accurate Identification ◽

Evolutionary Features ◽

Cross Covariance ◽

Antioxidant Proteins

Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.

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iT4SE-EP: Accurate Identification of Bacterial Type IV Secreted Effectors by Exploring Evolutionary Features from Two PSI-BLAST Profiles

Molecules ◽

10.3390/molecules26092487 ◽

2021 ◽

Vol 26 (9) ◽

pp. 2487

Author(s):

Haitao Han ◽

Chenchen Ding ◽

Xin Cheng ◽

Xiuzhi Sang ◽

Taigang Liu

Keyword(s):

Evolutionary Information ◽

Support Vector ◽

Target Cells ◽

Secretion Systems ◽

Accurate Identification ◽

Feature Selection Technique ◽

Type Iv ◽

Evolutionary Features ◽

Wide Range ◽

Encoding Strategies

Many gram-negative bacteria use type IV secretion systems to deliver effector molecules to a wide range of target cells. These substrate proteins, which are called type IV secreted effectors (T4SE), manipulate host cell processes during infection, often resulting in severe diseases or even death of the host. Therefore, identification of putative T4SEs has become a very active research topic in bioinformatics due to its vital roles in understanding host-pathogen interactions. PSI-BLAST profiles have been experimentally validated to provide important and discriminatory evolutionary information for various protein classification tasks. In the present study, an accurate computational predictor termed iT4SE-EP was developed for identifying T4SEs by extracting evolutionary features from the position-specific scoring matrix and the position-specific frequency matrix profiles. First, four types of encoding strategies were designed to transform protein sequences into fixed-length feature vectors based on the two profiles. Then, the feature selection technique based on the random forest algorithm was utilized to reduce redundant or irrelevant features without much loss of information. Finally, the optimal features were input into a support vector machine classifier to carry out the prediction of T4SEs. Our experimental results demonstrated that iT4SE-EP outperformed most of existing methods based on the independent dataset test.

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Hybrid supervised clustering based ensemble scheme for text classification

Kybernetes ◽

10.1108/k-10-2016-0300 ◽

2017 ◽

Vol 46 (2) ◽

pp. 330-348 ◽

Cited By ~ 7

Author(s):

Aytug Onan

Keyword(s):

Ensemble Learning ◽

Text Classification ◽

Predictive Performance ◽

Majority Voting ◽

Classifier Ensemble ◽

Support Vector ◽

Classification Algorithms ◽

Learning Methods ◽

Content Type ◽

Supervised Clustering

Purpose The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design. Design/methodology/approach An ensemble scheme based on hybrid supervised clustering is presented for text classification. In the presented scheme, supervised hybrid clustering, which is based on cuckoo search algorithm and k-means, is introduced to partition the data samples of each class into clusters so that training subsets with higher diversities can be provided. Each classifier is trained on the diversified training subsets and the predictions of individual classifiers are combined by the majority voting rule. The predictive performance of the proposed classifier ensemble is compared to conventional classification algorithms (such as Naïve Bayes, logistic regression, support vector machines and C4.5 algorithm) and ensemble learning methods (such as AdaBoost, bagging and random subspace) using 11 text benchmarks. Findings The experimental results indicate that the presented classifier ensemble outperforms the conventional classification algorithms and ensemble learning methods for text classification. Originality/value The presented ensemble scheme is the first to use supervised clustering to obtain diverse ensemble for text classification

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Identifying Phage Virion Proteins by Using Two-Step Feature Selection Methods

Molecules ◽

10.3390/molecules23082000 ◽

2018 ◽

Vol 23 (8) ◽

pp. 2000 ◽

Cited By ~ 15

Author(s):

Jiu-Xin Tan ◽

Fu-Ying Dao ◽

Hao Lv ◽

Peng-Mian Feng ◽

Hui Ding

Keyword(s):

Feature Selection ◽

Cross Validation ◽

Support Vector ◽

Virion Protein ◽

Accurate Identification ◽

Machine Learning Methods ◽

Minimal Redundancy ◽

Maximal Relevance ◽

Optimal Feature ◽

Fold Cross Validation

Accurate identification of phage virion protein is not only a key step for understanding the function of the phage virion protein but also helpful for further understanding the lysis mechanism of the bacterial cell. Since traditional experimental methods are time-consuming and costly for identifying phage virion proteins, it is extremely urgent to apply machine learning methods to accurately and efficiently identify phage virion proteins. In this work, a support vector machine (SVM) based method was proposed by mixing multiple sets of optimal g-gap dipeptide compositions. The analysis of variance (ANOVA) and the minimal-redundancy-maximal-relevance (mRMR) with an increment feature selection (IFS) were applied to single out the optimal feature set. In the five-fold cross-validation test, the proposed method achieved an overall accuracy of 87.95%. We believe that the proposed method will become an efficient and powerful method for scientists concerning phage virion proteins.

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Prediction of Incident Cancers in the Lifelines Population-Based Cohort

Cancers ◽

10.3390/cancers13092133 ◽

2021 ◽

Vol 13 (9) ◽

pp. 2133

Author(s):

Francisco O. Cortés-Ibañez ◽

Sunil Belur Nagaraj ◽

Ludo Cornelissen ◽

Gerjan J. Navis ◽

Bert van der Vegt ◽

...

Keyword(s):

Cancer Incidence ◽

Binary Classification ◽

Predictive Performance ◽

Population Based ◽

Support Vector ◽

Clinical Variables ◽

Incident Cancer ◽

History Of ◽

Diagnosis Of Cancer ◽

Auc Value

Cancer incidence is rising, and accurate prediction of incident cancers could be relevant to understanding and reducing cancer incidence. The aim of this study was to develop machine learning (ML) models that could predict an incident diagnosis of cancer. Participants without any history of cancer within the Lifelines population-based cohort were followed for a median of 7 years. Data were available for 116,188 cancer-free participants and 4232 incident cancer cases. At baseline, socioeconomic, lifestyle, and clinical variables were assessed. The main outcome was an incident cancer during follow-up (excluding skin cancer), based on linkage with the national pathology registry. The performance of three ML algorithms was evaluated using supervised binary classification to identify incident cancers among participants. Elastic net regularization and Gini index were used for variables selection. An overall area under the receiver operator curve (AUC) <0.75 was obtained, the highest AUC value was for prostate cancer (random forest AUC = 0.82 (95% CI 0.77–0.87), logistic regression AUC = 0.81 (95% CI 0.76–0.86), and support vector machines AUC = 0.83 (95% CI 0.78–0.88), respectively); age was the most important predictor in these models. Linear and non-linear ML algorithms including socioeconomic, lifestyle, and clinical variables produced a moderate predictive performance of incident cancers in the Lifelines cohort.

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Improved Estimation of Winter Wheat Aboveground Biomass Using Multiscale Textures Extracted from UAV-Based Digital Images and Hyperspectral Feature Analysis

Remote Sensing ◽

10.3390/rs13040581 ◽

2021 ◽

Vol 13 (4) ◽

pp. 581 ◽

Cited By ~ 2

Author(s):

Yuanyuan Fu ◽

Guijun Yang ◽

Xiaoyu Song ◽

Zhenhong Li ◽

Xingang Xu ◽

...

Keyword(s):

Winter Wheat ◽

Least Squares ◽

Aboveground Biomass ◽

Regression Models ◽

Digital Images ◽

Predictive Performance ◽

Estimation Accuracy ◽

Support Vector ◽

Biomass Estimation ◽

High Definition

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics “Mean” from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass.

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Creating a Chinese suicide dictionary for identifying suicide risk on social media

PeerJ ◽

10.7717/peerj.1455 ◽

2015 ◽

Vol 3 ◽

pp. e1455 ◽

Cited By ~ 10

Author(s):

Meizhen Lv ◽

Ang Li ◽

Tianli Liu ◽

Tingshao Zhu

Keyword(s):

Social Media ◽

Suicide Risk ◽

Classification Performance ◽

Support Vector ◽

Accurate Identification ◽

Vector Machines ◽

Social Media Service ◽

Linguistic Inquiry ◽

Suicide Prevention Programs ◽

Expert Ratings

Introduction.Suicide has become a serious worldwide epidemic. Early detection of individual suicide risk in population is important for reducing suicide rates. Traditional methods are ineffective in identifying suicide risk in time, suggesting a need for novel techniques. This paper proposes to detect suicide risk on social media using a Chinese suicide dictionary.Methods.To build the Chinese suicide dictionary, eight researchers were recruited to select initial words from 4,653 posts published on Sina Weibo (the largest social media service provider in China) and two Chinese sentiment dictionaries (HowNet and NTUSD). Then, another three researchers were recruited to filter out irrelevant words. Finally, remaining words were further expanded using a corpus-based method. After building the Chinese suicide dictionary, we tested its performance in identifying suicide risk on Weibo. First, we made a comparison of the performance in both detecting suicidal expression in Weibo posts and evaluating individual levels of suicide risk between the dictionary-based identifications and the expert ratings. Second, to differentiate between individuals with high and non-high scores on self-rating measure of suicide risk (Suicidal Possibility Scale, SPS), we built Support Vector Machines (SVM) models on the Chinese suicide dictionary and the Simplified Chinese Linguistic Inquiry and Word Count (SCLIWC) program, respectively. After that, we made a comparison of the classification performance between two types of SVM models.Results and Discussion.Dictionary-based identifications were significantly correlated with expert ratings in terms of both detecting suicidal expression (r= 0.507) and evaluating individual suicide risk (r= 0.455). For the differentiation between individuals with high and non-high scores on SPS, the Chinese suicide dictionary (t1:F1= 0.48; t2:F1= 0.56) produced a more accurate identification than SCLIWC (t1:F1= 0.41; t2:F1= 0.48) on different observation windows.Conclusions.This paper confirms that, using social media, it is possible to implement real-time monitoring individual suicide risk in population. Results of this study may be useful to improve Chinese suicide prevention programs and may be insightful for other countries.

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An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

Royal Society Open Science ◽

10.1098/rsos.180160 ◽

2018 ◽

Vol 5 (9) ◽

pp. 180160 ◽

Cited By ~ 2

Author(s):

Jian-hong Yang ◽

Huai-ying Fang ◽

Ren-cheng Zhang ◽

Kai Yang

Keyword(s):

Low Voltage ◽

Detection Algorithm ◽

Recognition Algorithm ◽

Support Vector ◽

Signal Loss ◽

Current Waveform ◽

Accurate Identification ◽

Metallic Contacts ◽

Vector Machines ◽

Integral Characteristics

Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.

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