A novel features ranking metric with application to scalable visual and bioinformatics data classification

UsIL-6: An unbalanced learning strategy for identifying IL-6 inducing peptides by undersampling technique

BACKGROUND AND OBJECTIVE

Interleukin-6 (IL-6) is the critical factor of early warning, monitoring, and prognosis in the inflammatory storm of COVID-19 cases. IL-6 inducing peptides, which can induce cytokine IL-6 production, are very important for the development of diagnosis and immunotherapy. Although the existing methods have some success in predicting IL-6 inducing peptides, there is still room for improvement in the performance of these models in practical application.

METHODS

In this study, we proposed UsIL-6, a high-performance bioinformatics tool for identifying IL-6 inducing peptides. First, we extracted five groups of physicochemical properties and sequence structural information from IL-6 inducing peptide sequences, and obtained a 636-dimensional feature vector, we also employed NearMiss3 undersampling method and normalization method StandardScaler to process the data. Then, a 40-dimensional optimal feature vector was obtained by Boruta feature selection method. Finally, we combined this feature vector with extreme randomization tree classifier to build the final model UsIL-6.

RESULTS

The AUC value of UsIL-6 on the independent test dataset was 0.87, and the BACC value was 0.808, which indicated that UsIL-6 had better performance than the existing methods in IL-6 inducing peptide recognition.

CONCLUSIONS

The performance comparison on independent test dataset confirmed that UsIL-6 could achieve the highest performance, best robustness, and most excellent generalization ability. We hope that UsIL-6 will become a valuable method to identify, annotate and characterize new IL-6 inducing peptides.

ACP-ML: A sequence-based method for anticancer peptide prediction

Cancer is a serious malignant tumor and is difficult to cure. Chemotherapy, as a primary treatment for cancer, causes significant harm to normal cells in the body and is often accompanied by serious side effects. Recently, anti-cancer peptides (ACPs) as a type of protein for treating cancers dominated research into the development of new anti-tumor drugs because of their ability to specifically target and destroy cancer cells. The screening of proteins with cancer-inhibiting properties from a large pool of proteins is key to the development of anti-tumor drugs. However, it is expensive and inefficient to accurately identify protein functions only through biological experiments due to their complex structure. Therefore, we propose a new prediction model ACP-ML to effectively predict ACPs. In terms of feature extraction, DPC, PseAAC, CTDC, CTDT and CS-Pse-PSSM features were used and the most optimal feature set was selected by comparing combinations of these features. Then, a two-step feature selection process using MRMD and RFE algorithms was performed to determine the most crucial features from the most optimal feature set for identifying ACPs. Furthermore, we assessed the classification accuracy of single learning models and different strategies-based ensemble models through ten-fold cross-validation. Ultimately, a voting-based ensemble learning method is developed to predict ACPs. To validate its effectiveness, two independent test sets were used to perform tests, achieving accuracy of 90.891 % and 92.578 % respectively. Compared with existing anticancer peptide prediction algorithms, the proposed feature processing method is more effective, and the proposed ensemble model ACP-ML exhibits stronger generalization capability and higher accuracy.

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction

DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-binding proteins play a crucial role in cellular and molecular biology, as they are essential for maintaining normal cellular physiological functions and adapting to environmental changes. The prediction of DNA-binding proteins has been a hot topic in the field of bioinformatics. The key to accurately classifying DNA-binding proteins is to find suitable feature sources and explore the information they contain. Although there are already many models for predicting DNA-binding proteins, there is still room for improvement in mining feature source information and calculation methods. In this study, we created a model called DBPboost to better identify DNA-binding proteins. The innovation of this study lies in the use of eight feature extraction methods, the improvement of the feature selection step, which involves selecting some features first and then performing feature selection again after feature fusion, and the optimization of the differential evolution algorithm in feature fusion, which improves the performance of feature fusion. The experimental results show that the prediction accuracy of the model on the UniSwiss dataset is 89.32%, and the sensitivity is 89.01%, which is better than most existing models.

Oral_voting_transfer: classification of oral microorganisms' function proteins with voting transfer model

Introduction

The oral microbial group typically represents the human body's highly complex microbial group ecosystem. Oral microorganisms take part in human diseases, including Oral cavity inflammation, mucosal disease, periodontal disease, tooth decay, and oral cancer. On the other hand, oral microbes can also cause endocrine disorders, digestive function, and nerve function disorders, such as diabetes, digestive system diseases, and Alzheimer's disease. It was noted that the proteins of oral microbes play significant roles in these serious diseases. Having a good knowledge of oral microbes can be helpful in analyzing the procession of related diseases. Moreover, the high-dimensional features and imbalanced data lead to the complexity of oral microbial issues, which can hardly be solved with traditional experimental methods.

Methods

To deal with these challenges, we proposed a novel method, which is oral_voting_transfer, to deal with such classification issues in the field of oral microorganisms. Such a method employed three features to classify the five oral microorganisms, including Streptococcus mutans, Staphylococcus aureus, abiotrophy adjacent, bifidobacterial, and Capnocytophaga. Firstly, we utilized the highly effective model, which successfully classifies the organelle's proteins and transfers to deal with the oral microorganisms. And then, some classification methods can be treated as the local classifiers in this work. Finally, the results are voting from the transfer classifiers and the voting ones.

Results and discussion

The proposed method achieved the well performances in the five oral microorganisms. The oral_voting_transfer is a standalone tool, and all its source codes are publicly available at https://github.com/baowz12345/voting_transfer.

IIFS: An improved incremental feature selection method for protein sequence processing

MOTIVATION

Discrete features can be obtained from protein sequences using a feature extraction method. These features are the basis of downstream processing of protein data, but it is necessary to screen and select some important features from them as they generally have data redundancy.

RESULT

Here, we report IIFS, an improved incremental feature selection method that exploits a new subset search strategy to find the optimal feature set. IIFS combines nonadjacent sorting features to prevent the drawbacks of data explosion and excessive reliance on feature sorting results. The comparative experimental results on 27 feature sorting data show that IIFS can find more accurate and important features compared to existing methods.The IIFS approach also handles data redundancy more efficiently and finds more representative and discriminatory features while ensuring minimal feature dimensionality and good evaluation metrics. Moreover, we wrap this method and deploy it on a web server for access at http://112.124.26.17:8005/.

Accurate prediction and key protein sequence feature identification of cyclins

Cyclin proteins are a group of proteins that activate the cell cycle by forming complexes with cyclin-dependent kinases. Identifying cyclins correctly can provide key clues to understanding the function of cyclins. However, due to the low similarity between cyclin protein sequences, the advancement of a machine learning-based approach to identify cycles is urgently needed. In this study, cyclin protein sequence features were extracted using the profile-based auto-cross covariance method. Then the features were ranked and selected with maximum relevance-maximum distance (MRMD) 1.0 and MRMD2.0. Finally, the prediction model was assessed through 10-fold cross-validation. The computational experiments showed that the best protein sequence features generated by MRMD1.0 could correctly predict 98.2% of cyclins using the random forest (RF) classifier, whereas seven-dimensional key protein sequence features identified with MRMD2.0 could correctly predict 96.1% of cyclins, which was superior to previous studies on the same dataset both in terms of dimensionality and performance comparisons. Therefore, our work provided a valuable tool for identifying cyclins. The model data can be downloaded from https://github.com/YUshunL/cyclin.

Weak Monotonicity With Trend Analysis for Unsupervised Feature Evaluation

Performance in an engineering system tends to degrade over time due to a variety of wearing or ageing processes. In supervisory controlled processes there are typically many signals being monitored that may help to characterize performance degradation. It is preferred to select the least amount of information to obtain high quality of predictive analysis from a large amount of collected data, in which labeling the data is not always feasible. To this end a novel unsupervised feature selection method, robust with respect to significant measurement disturbances, is proposed using the notion of "weak monotonicity" (WM). The robustness of this notion makes it very attractive to identify the common trend in the presence of measurement noises and population variation from the collected data. Based on WM, a novel suitability indicator is proposed to evaluate the performance of each feature. This new indicator is then used to select the key features that contribute to the WM of a family of processes when noises and variations among processes exist. In order to evaluate the performance of the proposed framework of the WM and suitability, a comparative study with other nine state-of-the-arts unsupervised feature evaluation and selection methods is carried out on well-known benchmark datasets. The results show a promising performance of the proposed framework on unsupervised feature evaluation in the presence of measurement noises and population variations.

Machine learning-based antioxidant protein identification model: Progress and evaluation

Efficient and accurate identification of antioxidant proteins is of great significance. In recent years, many models for identifying antioxidant proteins have been proposed, but the low sensitivity and high dimensionality of the models are common problems. The generalization ability of the model needs to be improved. Researchers have tried different feature extraction algorithms and feature selection algorithms to obtain the most effective feature combination and have chosen more appropriate classification algorithms and tools to improve model performance. In this article, we systematically reviewed the data set of the most frequently used antioxidant proteins and the method selection for each step of model establishment and discussed the characteristics of each method. We have conducted a detailed analysis of recent research and believe that the practical ability and efficiency of model application can be improved by reducing model dimensions. The key to improving the performance of antioxidant protein recognition models in the future may lie in feature selection, so this paper also focuses on the combination of feature extraction and selection steps in the analysis of the model building process.

Prediction of thermophilic protein using 2-D general series correlation pseudo amino acid features

The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein identification studies have only focused on accuracy. The relationship between the features' meaning and the proteins' physicochemical properties has yet to be studied in depth. In this article, we focused on the relationship between the features and the thermal stability of thermophilic proteins. This method used 2-D general series correlation pseudo amino acid (SC-PseAAC-General) features and realized accuracy of 82.76% using the J48 classifier. In addition, this research found the presence of higher frequencies of glutamic acid in thermophilic proteins, which help thermophilic proteins maintain their thermal stability by forming hydrogen bonds and salt bridges that prevent denaturation at high temperatures.

CFNCM: Collaborative filtering neighborhood-based model for predicting miRNA-disease associations

MicroRNAs have a significant role in the emergence of various human disorders. Consequently, it is essential to understand the existing interactions between miRNAs and diseases, as this will help scientists better study and comprehend the diseases' biological mechanisms. Findings can be employed as biomarkers or drug targets to advance the detection, diagnosis, and treatment of complex human disorders by foretelling possible disease-related miRNAs. This study proposed a computational model for predicting potential miRNA-disease associations called the Collaborative Filtering Neighborhood-based Classification Model (CFNCM), in light of the shortcomings of conventional and biological experiments, which are expensive and time-consuming. The model generated integrated miRNA and disease similarity matrices using the validated associations and miRNA and disease similarity information and used them as the input features for CFNCM. To produce class labels, we first determined the association scores for brand-new pairs using user-based collaborative filtering. With zero as the threshold, the associations with scores >0 were labelled 1, indicating a potential positive association, otherwise, it is marked as 0. Then, we developed classification models using various machine-learning algorithms. By comparison, we discovered that the support vector machine (SVM) produced the best AUC of 0.96 with 10-fold cross-validation through the GridSearchCV technique for identifying optimal parameter values. In addition, the models were evaluated and verified by analyzing the top 50 breast and lung neoplasms-related miRNAs, of which 46 and 47 associations were verified in two authoritative databases, dbDEMC and miR2Disease.

DP-AOP: A novel SVM-based antioxidant proteins identifier

The identification of antioxidant proteins is a challenging yet meaningful task, as they can protect against the damage caused by some free radicals. In addition to time-consuming, laborious, and expensive experimental identification methods, efficient identification of antioxidant proteins through machine learning algorithms has become increasingly common. In recent years, researchers have proposed models for identifying antioxidant proteins; unfortunately, although the accuracy of models is already high, their sensitivity is too low, indicating the possibility of overfitting in the model. Therefore, we developed a new model called DP-AOP for the recognition of antioxidant proteins. We used the SMOTE algorithm to balance the dataset, selected Wei's proposed feature extraction algorithm to obtain 473 dimensional feature vectors, and based on the sorting function in MRMD, scored and ranked each feature to obtain a feature set with contribution values ranging from high to low. To effectively reduce the feature dimension, we combined the dynamic programming idea to make the local eight features the optimal subset. After obtaining the 36 dimensional feature vectors, we finally selected 17 features through experimental analysis. The SVM classification algorithm was used to implement the model through the libsvm tool. The model achieved satisfactory performance, with an accuracy rate of 91.076 %, SN of 96.4 %, SP of 85.8 %, MCC of 82.6 %, and F1 core of 91.5 %. Furthermore, we built a free web server to facilitate researchers' subsequent unfolding studies of antioxidant protein recognition. The website is http://112.124.26.17:8003/#/.

MRMD3.0: A Python Tool and Webserver for Dimensionality Reduction and Data Visualization via an Ensemble Strategy

Dimensionality reduction is a hot topic in machine learning that can help researchers find key features from complex medical or biological data, which is crucial for biological sequence research, drug development, etc. However, when applied to specific datasets, different dimensionality reduction methods generate different results, which produces instability and makes tuning the parameters a time-consuming task. Exploring high quality features, genes, or attributes from complex data is an important task and challenge. To ensure the efficiency, robustness, and accuracy of experiments, in this work, we developed a dimensionality reduction tool MRMD3.0 based on the ensemble strategy of link analysis. It is mainly divided into two steps: first, the ensemble method is used to integrate different feature ranking algorithms to calculate feature importance, and then the forward feature search strategy combined with cross-validation is used to explore the proper feature combination. Compared with the previously developed version, MRMD3.0 has added more link-based ensemble algorithms, including PageRank, HITS, LeaderRank, and TrustRank. At the same time, more feature ranking algorithms have been added, and their effect and calculation speed have been greatly improved. In addition, the newest version provides an interface used by each feature ranking method and five kinds of charts to help users analyze features. Finally, we also provide an online webserver to help researchers analyze the data. Availability and implementation Webserver: http://lab.malab.cn/soft/MRMDv3/home.html. GitHub: https://github.com/heshida01/MRMD3.0.

DE-MHAIPs: Identification of SARS-CoV-2 phosphorylation sites based on differential evolution multi-feature learning and multi-head attention mechanism

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) around the world affects the normal lives of people all over the world. The computational methods can be used to accurately identify SARS-CoV-2 phosphorylation sites. In this paper, a new prediction model of SARS-CoV-2 phosphorylation sites, called DE-MHAIPs, is proposed. First, we use six feature extraction methods to extract protein sequence information from different perspectives. For the first time, we use a differential evolution (DE) algorithm to learn individual feature weights and fuse multi-information in a weighted combination. Next, Group LASSO is used to select a subset of good features. Then, the important protein information is given higher weight through multi-head attention. After that, the processed data is fed into long short-term memory network (LSTM) to further enhance model's ability to learn features. Finally, the data from LSTM are input into fully connected neural network (FCN) to predict SARS-CoV-2 phosphorylation sites. The AUC values of the S/T and Y datasets under 5-fold cross-validation reach 91.98% and 98.32%, respectively. The AUC values of the two datasets on the independent test set reach 91.72% and 97.78%, respectively. The experimental results show that the DE-MHAIPs method exhibits excellent predictive ability compared with other methods.

Stack-VTP: prediction of vesicle transport proteins based on stacked ensemble classifier and evolutionary information

Vesicle transport proteins not only play an important role in the transmembrane transport of molecules, but also have a place in the field of biomedicine, so the identification of vesicle transport proteins is particularly important. We propose a method based on ensemble learning and evolutionary information to identify vesicle transport proteins. Firstly, we preprocess the imbalanced dataset by random undersampling. Secondly, we extract position-specific scoring matrix (PSSM) from protein sequences, and then further extract AADP-PSSM and RPSSM features from PSSM, and use the Max-Relevance-Max-Distance (MRMD) algorithm to select the optimal feature subset. Finally, the optimal feature subset is fed into the stacked classifier for vesicle transport proteins identification. The experimental results show that the of accuracy (ACC), sensitivity (SN) and specificity (SP) of our method on the independent testing set are 82.53%, 0.774 and 0.836, respectively. The SN, SP and ACC of our proposed method are 0.013, 0.007 and 0.76% higher than the current state-of-the-art methods.

Predicting the efficacy of radiotherapy for esophageal squamous cell carcinoma based on enhanced computed tomography radiomics and combined models

Purpose

This study aimed to investigate the ability of enhanced computed tomography (CT)-based radiomics and dosimetric parameters in predicting response to radiotherapy for esophageal cancer.

Methods

A retrospective analysis of 147 patients diagnosed with esophageal cancer was performed, and the patients were divided into a training group (104 patients) and a validation group (43 patients). In total, 851 radiomics features were extracted from the primary lesions for analysis. Maximum correlation minimum redundancy and minimum least absolute shrinkage and selection operator were utilized for feature screening of radiomics features, and logistic regression was applied to construct a radiotherapy radiomics model for esophageal cancer. Finally, univariate and multivariate parameters were used to identify significant clinical and dosimetric characteristics for constructing combination models. The area evaluated the predictive performance under the receiver operating characteristics (AUC) curve and the accuracy, sensitivity, and specificity of the training and validation cohorts.

Results

Univariate logistic regression analysis revealed statistically significant differences in clinical parameters of sex (p=0.031) and esophageal cancer thickness (p=0.028) on treatment response, whereas dosimetric parameters did not differ significantly in response to treatment. The combined model demonstrated improved discrimination between the training and validation groups, with AUCs of 0.78 (95% confidence interval [CI], 0.69–0.87) and 0.79 (95% CI, 0.65–0.93) in the training and validation groups, respectively.

Conclusion

The combined model has potential application value in predicting the treatment response of patients with esophageal cancer after radiotherapy.

RF_phage virion: Classification of phage virion proteins with a random forest model

Introduction: Phages play essential roles in biological procession, and the virion proteins encoded by the phage genome constitute critical elements of the assembled phage particle. Methods: This study uses machine learning methods to classify phage virion proteins. We proposed a novel approach, RF_phage virion, for the effective classification of the virion and non-virion proteins. The model uses four protein sequence coding methods as features, and the random forest algorithm was employed to solve the classification problem. Results: The performance of the RF_phage virion model was analyzed by comparing the performance of this algorithm with that of classical machine learning methods. The proposed method achieved a specificity (Sp) of 93.37%%, sensitivity (Sn) of 90.30%, accuracy (Acc) of 91.84%, Matthews correlation coefficient (MCC) of .8371, and an F1 score of .9196.

iRNA-ac4C: A novel computational method for effectively detecting N4-acetylcytidine sites in human mRNA

RNA N4-acetylcytidine (ac4C) is the acetylation of cytidine at the nitrogen-4 position, which is a highly conserved RNA modification and involves a variety of biological processes. Hence, accurate identification of genome-wide ac4C sites is vital for understanding regulation mechanism of gene expression. In this work, a novel predictor, named iRNA-ac4C, was established to identify ac4C sites in human mRNA based on three feature extraction methods, including nucleotide composition, nucleotide chemical property, and accumulated nucleotide frequency. Subsequently, minimum-Redundancy-Maximum-Relevance combined with incremental feature selection strategies was utilized to select the optimal feature subset. According to the optimal feature subset, the best ac4C classification model was trained by gradient boosting decision tree with 10-fold cross-validation. The results of independent testing set indicated that our proposed method could produce encouraging generalization capabilities. For the convenience of other researchers, we established a user-friendly web server which is freely available at http://lin-group.cn/server/iRNA-ac4C/. We hope that the tool could provide guide for wet-experimental scholars.

Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE

BACKGROUND

Protein solubility is a precondition for efficient heterologous protein expression at the basis of most industrial applications and for functional interpretation in basic research. However, recurrent formation of inclusion bodies is still an inevitable roadblock in protein science and industry, where only nearly a quarter of proteins can be successfully expressed in soluble form. Despite numerous solubility prediction models having been developed over time, their performance remains unsatisfactory in the context of the current strong increase in available protein sequences. Hence, it is imperative to develop novel and highly accurate predictors that enable the prioritization of highly soluble proteins to reduce the cost of actual experimental work.

RESULTS

In this study, we developed a novel tool, DeepSoluE, which predicts protein solubility using a long-short-term memory (LSTM) network with hybrid features composed of physicochemical patterns and distributed representation of amino acids. Comparison results showed that the proposed model achieved more accurate and balanced performance than existing tools. Furthermore, we explored specific features that have a dominant impact on the model performance as well as their interaction effects.

CONCLUSIONS

DeepSoluE is suitable for the prediction of protein solubility in E. coli; it serves as a bioinformatics tool for prescreening of potentially soluble targets to reduce the cost of wet-experimental studies. The publicly available webserver is freely accessible at http://lab.malab.cn/~wangchao/softs/DeepSoluE/ .

A review of methods for predicting DNA N6-methyladenine sites

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

Utilize a few features to classify presynaptic and postsynaptic neurotoxins

Neurotoxins are a class of proteins that have a significant damaging effect on nerve tissue. Neurotoxins are classified into presynaptic neurotoxins and postsynaptic neurotoxins, and accurate identification of neurotoxins plays a key role in drug development. In this study, 90 presynaptic neurotoxins and 165 postsynaptic neurotoxins were classified. The features of the presynaptic and postsynaptic neurotoxin sequences were extracted using the AutoProp feature extraction method and feature selection was performed using the maximum relevance maximum distance (MRMD) program, Finally, only two features were retained to achieve 84.7% classification accuracy. Moreover, it was found that the two retained features were present in the conserved sites and motifs of presynaptic neurotoxins and could represent the critical structures of presynaptic neurotoxins. This method demonstrates that using a few key features to classify proteins can effectively identify critical protein structures.

Predicting Corynebacterium glutamicum promoters based on novel feature descriptor and feature selection technique

The promoter is an important noncoding DNA regulatory element, which combines with RNA polymerase to activate the expression of downstream genes. In industry, artificial arginine is mainly synthesized by Corynebacterium glutamicum. Replication of specific promoter regions can increase arginine production. Therefore, it is necessary to accurately locate the promoter in C. glutamicum. In the wet experiment, promoter identification depends on sigma factors and DNA splicing technology, this is a laborious job. To quickly and conveniently identify the promoters in C. glutamicum, we have developed a method based on novel feature representation and feature selection to complete this task, describing the DNA sequences through statistical parameters of multiple physicochemical properties, filtering redundant features by combining analysis of variance and hierarchical clustering, the prediction accuracy of the which is as high as 91.6%, the sensitivity of 91.9% can effectively identify promoters, and the specificity of 91.2% can accurately identify non-promoters. In addition, our model can correctly identify 181 promoters and 174 non-promoters among 400 independent samples, which proves that the developed prediction model has excellent robustness.

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms

Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo amino acid compositions, which were then carried out at two levels: categories and features. In brief, extra-tree was initially used for feature selection while SVM was then used to build the classification model. After that, the most suitable models for each category and each feature were selected by comparing with the three ensemble learning algorithms, that is, Random Forest, Adaboost, and Xgboost. According to our results, profile-based features were more suitable for building predictive models among the four categories. AUC value of the model constructed by Xgboost algorithm on independent data set could reach 92.66%. Moreover, Distance-based Residue, Physicochemical Distance Transformation and Profile-based Physicochemical Distance Transformation performed much better among the 17 features. AUC value of the Adaboost classifier constructed by Profile-based Physicochemical Distance Transformation on the independent dataset achieved 93.74%. Taken together, we proposed a better model with improved prediction capacity for protein-protein interactions between human and HCV in this study, which could provide practical reference for further experimental investigation into HCV-related diseases in future.Communicated by Ramaswamy H. Sarma.

Hybrid_DBP: Prediction of DNA-binding proteins using hybrid features and convolutional neural networks

DNA-binding proteins (DBP) play an essential role in the genetics and evolution of organisms. A particular DNA sequence could provide underlying therapeutic benefits for hereditary diseases and cancers. Studying these proteins can timely and effectively understand their mechanistic analysis and play a particular function in disease prevention and treatment. The limitation of identifying DNA-binding protein members from the sequence database is time-consuming, costly, and ineffective. Therefore, efficient methods for improving DBP classification are crucial to disease research. In this paper, we developed a novel predictor Hybrid _DBP, which identified potential DBP by using hybrid features and convolutional neural networks. The method combines two feature selection methods, MonoDiKGap and Kmer, and then used MRMD2.0 to remove redundant features. According to the results, 94% of DBP were correctly recognized, and the accuracy of the independent test set reached 91.2%. This means Hybrid_ DBP can become a useful prediction tool for predicting DBP.

A Novel Nonparametric Feature Selection Approach Based on Mutual Information Transfer Network

The filter feature selection algorithm is habitually used as an effective way to reduce the computational cost of data analysis by selecting and implementing only a subset of original features into the study. Mutual information (MI) is a popular measurement adopted to quantify the dependence among features. MI-based greedy forward methods (MIGFMs) have been widely applied to escape from computational complexity and exhaustion of high-dimensional data. However, most MIGFMs are parametric methods that necessitate proper preset parameters and stopping criteria. Improper parameters may lead to ignorance of better results. This paper proposes a novel nonparametric feature selection method based on mutual information and mixed-integer linear programming (MILP). By forming a mutual information network, we transform the feature selection problem into a maximum flow problem, which can be solved with the Gurobi solver in a reasonable time. The proposed method attempts to prevent negligence on obtaining a superior feature subset while keeping the computational cost in an affordable range. Analytical comparison of the proposed method with six feature selection methods reveals significantly better results compared to MIGFMs, considering classification accuracy.

omicsGAT: Graph Attention Network for Cancer Subtype Analyses

The use of high-throughput omics technologies is becoming increasingly popular in all facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative measures of more than tens of thousands of biological features. It provides a more comprehensive molecular perspective of studied cancer mechanisms compared to traditional approaches. Graph-based learning models have been proposed to learn important hidden representations from gene expression data and network structure to improve cancer outcome prediction, patient stratification, and cell clustering. However, these graph-based methods cannot rank the importance of the different neighbors for a particular sample in the downstream cancer subtype analyses. In this study, we introduce omicsGAT, a graph attention network (GAT) model to integrate graph-based learning with an attention mechanism for RNA-seq data analysis. The multi-head attention mechanism in omicsGAT can more effectively secure information of a particular sample by assigning different attention coefficients to its neighbors. Comprehensive experiments on The Cancer Genome Atlas (TCGA) breast cancer and bladder cancer bulk RNA-seq data and two single-cell RNA-seq datasets validate that (1) the proposed model can effectively integrate neighborhood information of a sample and learn an embedding vector to improve disease phenotype prediction, cancer patient stratification, and cell clustering of the sample and (2) the attention matrix generated from the multi-head attention coefficients provides more useful information compared to the sample correlation-based adjacency matrix. From the results, we can conclude that some neighbors play a more important role than others in cancer subtype analyses of a particular sample based on the attention coefficient.

Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Long non-coding RNA (lncRNA) contains short open reading frames (sORFs), and sORFs-encoded short peptides (SEPs) have become the focus of scientific studies due to their crucial role in life activities. The identification of SEPs is vital to further understanding their regulatory function. Bioinformatics methods can quickly identify SEPs to provide credible candidate sequences for verifying SEPs by biological experimenrts. However, there is a lack of methods for identifying SEPs directly. In this study, a machine learning method to identify SEPs of plant lncRNA (ISPL) is proposed. Hybrid features including sequence features and physicochemical features are extracted manually or adaptively to construct different modal features. In order to keep the stability of feature selection, the non-linear correction applied in Max-Relevance-Max-Distance (nocRD) feature selection method is proposed, which integrates multiple feature ranking results and uses the iterative random forest for different modal features dimensionality reduction. Classification models with different modal features are constructed, and their outputs are combined for ensemble classification. The experimental results show that the accuracy of ISPL is 89.86% percent on the independent test set, which will have important implications for further studies of functional genomic.

Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome

OBJECTIVE

The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models.

METHODS

The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model.

RESULTS

Eighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase.

CONCLUSION

The model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8⁺ T-cell-related host adaptive immune response and with the available positive and negative CD8⁺ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8⁺ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8⁺ TCEs from non-CD8⁺ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8⁺ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

Ensemble Learning-Based Feature Selection for Phage Protein Prediction

Phage has high specificity for its host recognition. As a natural enemy of bacteria, it has been used to treat super bacteria many times. Identifying phage proteins from the original sequence is very important for understanding the relationship between phage and host bacteria and developing new antimicrobial agents. However, traditional experimental methods are both expensive and time-consuming. In this study, an ensemble learning-based feature selection method is proposed to find important features for phage protein identification. The method uses four types of protein sequence-derived features, quantifies the importance of each feature by adding perturbations to the features to influence the results, and finally splices the important features among the four types of features. In addition, we analyzed the selected features and their biological significance.

SgRNA-RF: Identification of SgRNA On-Target Activity With Imbalanced Datasets

Single-guide RNA is a guide RNA (gRNA), which guides the insertion or deletion of uridine residues into kinetoplastid during RNA editing. It is a small non-coding RNA that can be combined with pre -mRNA pairing. SgRNA is a critical component of the CRISPR/Cas9 gene knockout system and play an important role in gene editing and gene regulation. It is important to accurately and quickly identify highly on-target activity sgRNAs. Due to its importance, several computational predictors have been proposed to predict sgRNAs on-target activity. All these methods have clearly contributed to the development of this very important field. However, they also have certain limitations. In the paper, we developed a new classifier SgRNA-RF, which extracts the features of nucleic acid composition and structure of on-target activity sgRNA sequence and identified by random forest algorithm. In addition to solving an imbalanced dataset, this paper proposed a new method called CS-Smote. We compared sgRNA-RF with state-of-the-art predictors on the five datasets, and found SgRNA-RF significantly improved the identification accuracy, with accuracies of 0.8636,0.9161,0.894,0.938,0.965,0.77,0.979,0.973, respectively. The user-friendly web server that implements sgRNA-RF is freely available at http://server.malab.cn/sgRNA-RF/.

Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks

MOTIVATION

Phytopathogenic fungi secrete effector proteins to subvert host defenses and facilitate infection. Systematic analysis and prediction of candidate fungal effector proteins is crucial for experimental validation and biological control of plant disease. However, two problems are still considered intractable to be solved in fungal effector prediction: one is the high-level diversity in effector sequences that increases the difficulty of protein feature learning, and the other is the class imbalance between effector and non-effector samples in the training dataset.

RESULTS

In our study, pretrained deep representation learning methods are presented to represent multiple characteristics of sequences for predicting fungal effectors and generative adversarial networks are adapted to create synthetic feature samples to address the data imbalance problem. Compared with the state-of-the-art fungal effector prediction methods, Effector-GAN shows an overall improvement in accuracy in the independent test set.

AVAILABILITY AND IMPLEMENTATION

Effector-GAN offers a user-friendly interface to inspect potential fungal effector proteins (http://lab.malab.cn/~wys/webserver/Effector-GAN). The Python script can be downloaded from http://lab.malab.cn/~wys/gitlab/effector-gan.

Prediction of Plant Resistance Proteins Based on Pairwise Energy Content and Stacking Framework

Plant resistance proteins (R proteins) recognize effector proteins secreted by pathogenic microorganisms and trigger an immune response against pathogenic microbial infestation. Accurate identification of plant R proteins is an important research topic in plant pathology. Plant R protein prediction has achieved many research results. Recently, some machine learning-based methods have emerged to identify plant R proteins. Still, most of them only rely on protein sequence features, which ignore inter-amino acid features, thus limiting the further improvement of plant R protein prediction performance. In this manuscript, we propose a method called StackRPred to predict plant R proteins. Specifically, the StackRPred first obtains plant R protein feature information from the pairwise energy content of residues; then, the obtained feature information is fed into the stacking framework for training to construct a prediction model for plant R proteins. The results of both the five-fold cross-validation and independent test validation show that our proposed method outperforms other state-of-the-art methods, indicating that StackRPred is an effective tool for predicting plant R proteins. It is expected to bring some favorable contribution to the study of plant R proteins.

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.

POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability

Mass spectrometry-based proteomic technique has become indispensable in current exploration of complex and dynamic biological processes. Instrument development has largely ensured the effective production of proteomic data, which necessitates commensurate advances in statistical framework to discover the optimal proteomic signature. Current framework mainly emphasizes the generalizability of the identified signature in predicting the independent data but neglects the reproducibility among signatures identified from independently repeated trials on different sub-dataset. These problems seriously restricted the wide application of the proteomic technique in molecular biology and other related directions. Thus, it is crucial to enable the generalizable and reproducible discovery of the proteomic signature with the subsequent indication of phenotype association. However, no such tool has been developed and available yet. Herein, an online tool, POSREG, was therefore constructed to identify the optimal signature for a set of proteomic data. It works by (i) identifying the proteomic signature of good reproducibility and aggregating them to ensemble feature ranking by ensemble learning, (ii) assessing the generalizability of ensemble feature ranking to acquire the optimal signature and (iii) indicating the phenotype association of discovered signature. POSREG is unique in its capacity of discovering the proteomic signature by simultaneously optimizing its reproducibility and generalizability. It is now accessible free of charge without any registration or login requirement at https://idrblab.org/posreg/.

Identify DNA-Binding Proteins Through the Extreme Gradient Boosting Algorithm

The exploration of DNA-binding proteins (DBPs) is an important aspect of studying biological life activities. Research on life activities requires the support of scientific research results on DBPs. The decline in many life activities is closely related to DBPs. Generally, the detection method for identifying DBPs is achieved through biochemical experiments. This method is inefficient and requires considerable manpower, material resources and time. At present, several computational approaches have been developed to detect DBPs, among which machine learning (ML) algorithm-based computational techniques have shown excellent performance. In our experiments, our method uses fewer features and simpler recognition methods than other methods and simultaneously obtains satisfactory results. First, we use six feature extraction methods to extract sequence features from the same group of DBPs. Then, this feature information is spliced together, and the data are standardized. Finally, the extreme gradient boosting (XGBoost) model is used to construct an effective predictive model. Compared with other excellent methods, our proposed method has achieved better results. The accuracy achieved by our method is 78.26% for PDB2272 and 85.48% for PDB186. The accuracy of the experimental results achieved by our strategy is similar to that of previous detection methods.

Immunoglobulin Classification Based on FC* and GC* Features

Immunoglobulins have a pivotal role in disease regulation. Therefore, it is vital to accurately identify immunoglobulins to develop new drugs and research related diseases. Compared with utilizing high-dimension features to identify immunoglobulins, this research aimed to examine a method to classify immunoglobulins and non-immunoglobulins using two features, FC* and GC*. Classification of 228 samples (109 immunoglobulin samples and 119 non-immunoglobulin samples) revealed that the overall accuracy was 80.7% in 10-fold cross-validation using the J48 classifier implemented in Weka software. The FC* feature identified in this study was found in the immunoglobulin subtype domain, which demonstrated that this extracted feature could represent functional and structural properties of immunoglobulins for forecasting.

VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost

Vesicular transport proteins are related to many human diseases, and they threaten human health when they undergo pathological changes. Protein function prediction has been one of the most in-depth topics in bioinformatics. In this work, we developed a useful tool to identify vesicular transport proteins. Our strategy is to extract transition probability composition, autocovariance transformation and other information from the position-specific scoring matrix as feature vectors. EditedNearesNeighbours (ENN) is used to address the imbalance of the data set, and the Max-Relevance-Max-Distance (MRMD) algorithm is adopted to reduce the dimension of the feature vector. We used 5-fold cross-validation and independent test sets to evaluate our model. On the test set, VTP-Identifier presented a higher performance compared with GRU. The accuracy, Matthew's correlation coefficient (MCC) and area under the ROC curve (AUC) were 83.6%, 0.531 and 0.873, respectively.

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

dPromoter-XGBoost: Detecting promoters and strength by combining multiple descriptors and feature selection using XGBoost

Promoters play an irreplaceable role in biological processes and genetics, which are responsible for stimulating the transcription and expression of specific genes. Promoter abnormalities have been found in some diseases, and the level of promoter-binding transcription factors can be used as a marker before a disease occurs. Hence, detecting promoters from DNA sequences has important biological significance, particular, distinguishing strong promoters can help to elucidate differences in gene expression and the mechanisms of specific diseases. With the introduction of third-generation sequencing, it is difficult to match the speed of sequencing to the speed of labeling promoters experimentally. Many computing models have been designed to fill this gap and identify unlabeled DNA. However, their feature representation methods are very singular, which cannot reflect the information contained in the original samples. With the aim of avoiding information loss, we propose a computational model based on multiple descriptors and feature selection to jointly express samples. It is worth mentioning that a new feature descriptor called K-mer word vector is defined. The promoter model of multiple feature descriptors dominated by K-mer word vector achieves similar performance to existing methods, the sensitivity of 85.72% can distinguish the promoter more effectively than other methods. Furthermore, the performance of the promoter strength has surpassed published methods, and accuracy of 77.00% greatly improves the ability to distinguish between strong and weak promoters.

Identification of plant vacuole proteins by exploiting deep representation learning features

Plant vacuoles are the most important organelles for plant growth, development, and defense, and they play an important role in many types of stress responses. An important function of vacuole proteins is the transport of various classes of amino acids, ions, sugars, and other molecules. Accurate identification of vacuole proteins is crucial for revealing their biological functions. Several automatic and rapid computational tools have been proposed for the subcellular localization of proteins. Regrettably, they are not specific for the identification of plant vacuole proteins. To the best of our knowledge, there is only one computational software specifically trained for plant vacuolar proteins. Although its accuracy is acceptable, the prediction performance and stability of this method in practical applications can still be improved. Hence, in this study, a new predictor named iPVP-DRLF was developed to identify plant vacuole proteins specifically and effectively. This prediction software is designed using the light gradient boosting machine (LGBM) algorithm and hybrid features composed of classic sequence features and deep representation learning features. iPVP-DRLF achieved fivefold cross-validation and independent test accuracy values of 88.25 % and 87.16 %, respectively, both outperforming previous state-of-the-art predictors. Moreover, the blind dataset test results also showed that the performance of iPVP-DRLF was significantly better than the existing tools. The results of comparative experiments confirmed that deep representation learning features have an advantage over other classic sequence features in the identification of plant vacuole proteins. We believe that iPVP-DRLF would serve as an effective computational technique for plant vacuole protein prediction and facilitate related future research. The online server is freely accessible at https://lab.malab.cn/~acy/iPVP-DRLF. In addition, the source code and datasets are also accessible at https://github.com/jiaoshihu/iPVP-DRLF.

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

Staem5: A novel computational approachfor accurate prediction of m5C site

5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.

Prediction of Hormone-Binding Proteins Based on K-mer Feature Representation and Naive Bayes

Hormone binding protein (HBP) is a soluble carrier protein that interacts selectively with different types of hormones and has various effects on the body's life activities. HBPs play an important role in the growth process of organisms, but their specific role is still unclear. Therefore, correctly identifying HBPs is the first step towards understanding and studying their biological function. However, due to their high cost and long experimental period, it is difficult for traditional biochemical experiments to correctly identify HBPs from an increasing number of proteins, so the real characterization of HBPs has become a challenging task for researchers. To measure the effectiveness of HBPs, an accurate and reliable prediction model for their identification is desirable. In this paper, we construct the prediction model HBP_NB. First, HBPs data were collected from the UniProt database, and a dataset was established. Then, based on the established high-quality dataset, the k-mer (K = 3) feature representation method was used to extract features. Second, the feature selection algorithm was used to reduce the dimensionality of the extracted features and select the appropriate optimal feature set. Finally, the selected features are input into Naive Bayes to construct the prediction model, and the model is evaluated by using 10-fold cross-validation. The final results were 95.45% accuracy, 94.17% sensitivity and 96.73% specificity. These results indicate that our model is feasible and effective.