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Kuang H, Zhang Z, Zeng B, Liu X, Zuo H, Xu X, Wang L. A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest. BMC Bioinformatics 2024; 25:78. [PMID: 38378437 PMCID: PMC10877932 DOI: 10.1186/s12859-024-05687-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 01/31/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experiments are very expensive and time-consuming. Therefore, it is meaningful to develop efficient computational models to forecast potential microbe-drug associations. RESULTS In this manuscript, we proposed a novel prediction model called GARFMDA by combining graph attention networks and bilayer random forest to infer probable microbe-drug correlations. In GARFMDA, through integrating different microbe-drug-disease correlation indices, we constructed two different microbe-drug networks first. And then, based on multiple measures of similarity, we constructed a unique feature matrix for drugs and microbes respectively. Next, we fed these newly-obtained microbe-drug networks together with feature matrices into the graph attention network to extract the low-dimensional feature representations for drugs and microbes separately. Thereafter, these low-dimensional feature representations, along with the feature matrices, would be further inputted into the first layer of the Bilayer random forest model to obtain the contribution values of all features. And then, after removing features with low contribution values, these contribution values would be fed into the second layer of the Bilayer random forest to detect potential links between microbes and drugs. CONCLUSIONS Experimental results and case studies show that GARFMDA can achieve better prediction performance than state-of-the-art approaches, which means that GARFMDA may be a useful tool in the field of microbe-drug association prediction in the future. Besides, the source code of GARFMDA is available at https://github.com/KuangHaiYue/GARFMDA.git.
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Affiliation(s)
- Haiyue Kuang
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China
| | - Zhen Zhang
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China.
| | - Bin Zeng
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China.
| | - Xin Liu
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China.
| | - Hao Zuo
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China
| | - Xingye Xu
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China
| | - Lei Wang
- Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, 410022, China.
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Zhu H, Hao H, Yu L. Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance. BMC Biol 2023; 21:294. [PMID: 38115088 PMCID: PMC10731776 DOI: 10.1186/s12915-023-01796-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for improvement. RESULTS In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribution of each section of our model. Moreover, we conducted experiments on common drugs and case studies, including Alzheimer's disease, Crohn's disease, and colorectal neoplasms, to validate the effectiveness of our framework. CONCLUSIONS Significantly, our model exceeded other currently state-of-the-art methods, exhibiting a great improvement on the HMDAD database.
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Affiliation(s)
- Huan Zhu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Hongxia Hao
- School of Computer Science and Technology, Xidian University, Xi'an, China.
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
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3
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Li H, Hou ZJ, Zhang WG, Qu J, Yao HB, Chen Y. Prediction of potential drug-microbe associations based on matrix factorization and a three-layer heterogeneous network. Comput Biol Chem 2023; 104:107857. [PMID: 37018909 DOI: 10.1016/j.compbiolchem.2023.107857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/27/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023]
Abstract
Microbes in the human body are closely linked to many complex human diseases and are emerging as new drug targets. These microbes play a crucial role in drug development and disease treatment. Traditional methods of biological experiments are not only time-consuming but also costly. Using computational methods to predict microbe-drug associations can effectively complement biological experiments. In this experiment, we constructed heterogeneity networks for drugs, microbes, and diseases using multiple biomedical data sources. Then, we developed a model with matrix factorization and a three-layer heterogeneous network (MFTLHNMDA) to predict potential drug-microbe associations. The probability of microbe-drug association was obtained by a global network-based update algorithm. Finally, the performance of MFTLHNMDA was evaluated in the framework of leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV). The results showed that our model performed better than six state-of-the-art methods that had AUC of 0.9396 and 0.9385 + /- 0.0000, respectively. This case study further confirms the effectiveness of MFTLHNMDA in identifying potential drug-microbe associations and new drug-microbe associations.
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Shokri Garjan H, Omidi Y, Poursheikhali Asghari M, Ferdousi R. In-silico computational approaches to study microbiota impacts on diseases and pharmacotherapy. Gut Pathog 2023; 15:10. [PMID: 36882861 PMCID: PMC9990230 DOI: 10.1186/s13099-023-00535-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 02/21/2023] [Indexed: 03/09/2023] Open
Abstract
Microorganisms have been linked to a variety of critical human disease, thanks to advances in sequencing technology and microbiology. The growing recognition of human microbe-disease relationships provides crucial insights into the underlying disease process from the perspective of pathogens, which is extremely useful for pathogenesis research, early diagnosis, and precision medicine and therapy. Microbe-based analysis in terms of diseases and related drug discovery can predict new connections/mechanisms and provide new concepts. These phenomena have been studied via various in-silico computational approaches. This review aims to elaborate on the computational works conducted on the microbe-disease and microbe-drug topics, discuss the computational model approaches used for predicting associations and provide comprehensive information on the related databases. Finally, we discussed potential prospects and obstacles in this field of study, while also outlining some recommendations for further enhancing predictive capabilities.
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Affiliation(s)
- Hassan Shokri Garjan
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Yadollah Omidi
- Department of Pharmaceutical Sciences, Nova Southeastern University, College of Pharmacy, Fort Lauderdale, FL, USA
| | | | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
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Liu H, Bing P, Zhang M, Tian G, Ma J, Li H, Bao M, He K, He J, He B, Yang J. MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm. Comput Struct Biotechnol J 2023; 21:1414-1423. [PMID: 36824227 PMCID: PMC9941872 DOI: 10.1016/j.csbj.2022.12.053] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/03/2023] Open
Abstract
Identifying the potential associations between microbes and diseases is the first step for revealing the pathological mechanisms of microbe-associated diseases. However, traditional culture-based microbial experiments are expensive and time-consuming. Thus, it is critical to prioritize disease-associated microbes by computational methods for further experimental validation. In this study, we proposed a novel method called MNNMDA, to predict microbe-disease associations (MDAs) by applying a Matrix Nuclear Norm method into known microbe and disease data. Specifically, we first calculated Gaussian interaction profile kernel similarity and functional similarity for diseases and microbes. Then we constructed a heterogeneous information network by combining the integrated disease similarity network, the integrated microbe similarity network and the known microbe-disease bipartite network. Finally, we formulated the microbe-disease association prediction problem as a low-rank matrix completion problem, which was solved by minimizing the nuclear norm of a matrix with a few regularization terms. We tested the performances of MNNMDA in three datasets including HMDAD, Disbiome, and Combined Data with small, medium and large sizes respectively. We also compared MNNMDA with 5 state-of-the-art methods including KATZHMDA, LRLSHMDA, NTSHMDA, GATMDA, and KGNMDA, respectively. MNNMDA achieved area under the ROC curves (AUROC) of 0.9536 and 0.9364 respectively on HDMAD and Disbiome, better than the AUCs of compared methods under the 5-fold cross-validation for all microbe-disease associations. It also obtained a relatively good performance with AUROC 0.8858 in the combined data. In addition, MNNMDA was also better than other methods in area under precision and recall curve (AUPR) under the 5-fold cross-validation for all associations, and in both AUROC and AUPR under the 5-fold cross-validation for diseases and the 5-fold cross-validation for microbes. Finally, the case studies on colon cancer and inflammatory bowel disease (IBD) also validated the effectiveness of MNNMDA. In conclusion, MNNMDA is an effective method in predicting microbe-disease associations. Availability The codes and data for this paper are freely available at Github https://github.com/Haiyan-Liu666/MNNMDA.
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Affiliation(s)
- Haiyan Liu
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,College of Information Engineering, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China
| | - Meijun Zhang
- Geneis Beijing Co., Ltd., Beijing 100102, PR China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing 100102, PR China
| | - Jun Ma
- College of Information Engineering, Changsha Medical University, Changsha 410219, PR China
| | - Haigang Li
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Meihua Bao
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Kunhui He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China
| | - Jianjun He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
| | - Jialiang Yang
- Academician Workstation, Changsha Medical University, Changsha 410219, PR China,Hunan Key Laboratory of the Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha 410219, PR China,Geneis Beijing Co., Ltd., Beijing 100102, PR China,School of pharmacy, Changsha Medical University, Changsha 410219, PR China,Corresponding authors at: Academician Workstation, Changsha Medical University, Changsha 410219, PR China.
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Tan Y, Zou J, Kuang L, Wang X, Zeng B, Zhang Z, Wang L. GSAMDA: a computational model for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder. BMC Bioinformatics 2022; 23:492. [PMID: 36401174 PMCID: PMC9673879 DOI: 10.1186/s12859-022-05053-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
Background Clinical studies show that microorganisms are closely related to human health, and the discovery of potential associations between microbes and drugs will facilitate drug research and development. However, at present, few computational methods for predicting microbe–drug associations have been proposed.
Results In this work, we proposed a novel computational model named GSAMDA based on the graph attention network and sparse autoencoder to infer latent microbe–drug associations. In GSAMDA, we first built a heterogeneous network through integrating known microbe–drug associations, microbe similarities and drug similarities. And then, we adopted a GAT-based autoencoder and a sparse autoencoder module respectively to learn topological representations and attribute representations for nodes in the newly constructed heterogeneous network. Finally, based on these two kinds of node representations, we constructed two kinds of feature matrices for microbes and drugs separately, and then, utilized them to calculate possible association scores for microbe–drug pairs. Conclusion A novel computational model is proposed for predicting potential microbe–drug associations based on graph attention network and sparse autoencoder. Compared with other five state-of-the-art competitive methods, the experimental results illustrated that our model can achieve better performance. Moreover, case studies on two categories of representative drugs and microbes further demonstrated the effectiveness of our model as well.
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Hua M, Yu S, Liu T, Yang X, Wang H. MVGCNMDA: Multi-view Graph Augmentation Convolutional Network for Uncovering Disease-Related Microbes. Interdiscip Sci 2022; 14:669-682. [PMID: 35428964 DOI: 10.1007/s12539-022-00514-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/06/2022] [Accepted: 03/13/2022] [Indexed: 06/14/2023]
Abstract
MOTIVATION Exploring the interrelationships between microbes and disease can help microbiologists make decisions and plan treatments. Predicting new microbe-disease associations currently relies on biological experiments and domain knowledge, which is time-consuming and inefficient. Automated algorithms are used to uncover the intrinsic link between microbes and disease. However, due to data noise and inadequate understanding of relevant biology, the efficient prediction of microbe-disease associations is still crucial. This study develops a multi-view graph augmentation convolutional network (MVGCNMDA) to predict potential disease-associated microbes. METHODS First, we use two data augmentation methods, edge perturbation and node dropping, to remove the data noise in the preprocessing stage. Second, we calculate Gaussian interaction profile kernel similarity and cosine similarity. Therefore, the Graph Convolutional Network(GCN) can fully use multi-view features. Then, the multi-view features are fed into the multi-attention block to learn the weights of different features adaptively. Finally, the embedding results are obtained using a Convolutional Neural Network (CNN) combiner, and the matrix completion is used to predict the relationship between potential microbes and diseases. RESULTS We test our model on the Human microbe-disease Association Database (HMDAD), Disbiome, and the Combined Dataset (Peryton and MicroPhenoDB). The area under PR curve (AUPR), area under ROC curve (AUC), F1 score, and RECALL value are calculated to evaluate the performance of the developed MVGCNMDA. The AUPR is 0.9440, AUC is 0.9428, F1 score is 0.9383, and RECALL value is 0.8858. The experiments show that our model can accurately predict potential microbe-disease associations compared with the state-of-the-art works on the global Leave-One-Out-Cross-Validation (LOOCV) and the fivefold Cross-Validation (fivefold CV). To further verify the effectiveness of the proposed graph data augmentation, we designed five different settings in the ablation study. Furthermore, we present two case studies that validate the prediction of the potential association between microbes and diseases by MVGCNMDA.
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Affiliation(s)
- Meifang Hua
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Shengpeng Yu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Tianyu Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Xue Yang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Hong Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
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Wang L, Tan Y, Yang X, Kuang L, Ping P. Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models. Brief Bioinform 2022; 23:6553604. [PMID: 35325024 DOI: 10.1093/bib/bbac080] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 12/11/2022] Open
Abstract
In recent years, with the rapid development of techniques in bioinformatics and life science, a considerable quantity of biomedical data has been accumulated, based on which researchers have developed various computational approaches to discover potential associations between human microbes, drugs and diseases. This paper provides a comprehensive overview of recent advances in prediction of potential correlations between microbes, drugs and diseases from biological data to computational models. Firstly, we introduced the widely used datasets relevant to the identification of potential relationships between microbes, drugs and diseases in detail. And then, we divided a series of a lot of representative computing models into five major categories including network, matrix factorization, matrix completion, regularization and artificial neural network for in-depth discussion and comparison. Finally, we analysed possible challenges and opportunities in this research area, and at the same time we outlined some suggestions for further improvement of predictive performances as well.
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Affiliation(s)
- Lei Wang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Yaqin Tan
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Xiaoyu Yang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Linai Kuang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, Hunan, China
| | - Pengyao Ping
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, 410022, Hunan, China
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Xu D, Xu H, Zhang Y, Gao R. Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes. Front Microbiol 2022; 13:834982. [PMID: 35369503 PMCID: PMC8965656 DOI: 10.3389/fmicb.2022.834982] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 01/19/2022] [Indexed: 12/14/2022] Open
Abstract
Extensive clinical and biomedical studies have shown that microbiome plays a prominent role in human health. Identifying potential microbe–disease associations (MDAs) can help reveal the pathological mechanism of human diseases and be useful for the prevention, diagnosis, and treatment of human diseases. Therefore, it is necessary to develop effective computational models and reduce the cost and time of biological experiments. Here, we developed a novel machine learning-based joint framework called CWNMF-GLapRLS for human MDA prediction using the proposed collaborative weighted non-negative matrix factorization (CWNMF) technique and graph Laplacian regularized least squares. Especially, to fuse more similarity information, we calculated the functional similarity of microbes. To deal with missing values and effectively overcome the data sparsity problem, we proposed a collaborative weighted NMF technique to reconstruct the original association matrix. In addition, we developed a graph Laplacian regularized least-squares method for prediction. The experimental results of fivefold and leave-one-out cross-validation demonstrated that our method achieved the best performance by comparing it with 5 state-of-the-art methods on the benchmark dataset. Case studies further showed that the proposed method is an effective tool to predict potential MDAs and can provide more help for biomedical researchers.
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Affiliation(s)
- Da Xu
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Hanxiao Xu
- School of Mathematics and Statistics, Shandong University, Weihai, China
| | - Yusen Zhang
- School of Mathematics and Statistics, Shandong University, Weihai, China
- *Correspondence: Yusen Zhang,
| | - Rui Gao
- School of Control Science and Engineering, Shandong University, Jinan, China
- Rui Gao,
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