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Idrees S, Paudel KR, Sadaf T, Hansbro PM. How different viruses perturb host cellular machinery via short linear motifs. EXCLI JOURNAL 2023; 22:1113-1128. [PMID: 38054205 PMCID: PMC10694346 DOI: 10.17179/excli2023-6328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/18/2023] [Indexed: 12/07/2023]
Abstract
The virus interacts with its hosts by developing protein-protein interactions. Most viruses employ protein interactions to imitate the host protein: A viral protein with the same amino acid sequence or structure as the host protein attaches to the host protein's binding partner and interferes with the host protein's pathways. Being opportunistic, viruses have evolved to manipulate host cellular mechanisms by mimicking short linear motifs. In this review, we shed light on the current understanding of mimicry via short linear motifs and focus on viral mimicry by genetically different viral subtypes by providing recent examples of mimicry evidence and how high-throughput methods can be a reliable source to study SLiM-mediated viral mimicry.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Philip M. Hansbro
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
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2
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Sun J, Kulandaisamy A, Liu J, Hu K, Gromiha MM, Zhang Y. Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications. Comput Struct Biotechnol J 2023; 21:1205-1226. [PMID: 36817959 PMCID: PMC9932300 DOI: 10.1016/j.csbj.2023.01.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/16/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.
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Affiliation(s)
- Jianfeng Sun
- Botnar Research Centre, Nuffield Department of Orthopedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Headington, Oxford OX3 7LD, UK
| | - Arulsamy Kulandaisamy
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India
| | - Jacklyn Liu
- UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK
| | - Kai Hu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China
| | - M. Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of BioSciences, Indian Institute of Technology Madras, Chennai 600 036, Tamilnadu, India,Corresponding authors.
| | - Yuan Zhang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China,Corresponding authors.
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3
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An JY, Zhou Y, Zhao YJ, Yan ZJ. An Efficient Feature Extraction Technique Based on Local Coding PSSM and Multifeatures Fusion for Predicting Protein-Protein Interactions. Evol Bioinform Online 2019; 15:1176934319879920. [PMID: 31619921 PMCID: PMC6777060 DOI: 10.1177/1176934319879920] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 09/11/2019] [Indexed: 12/20/2022] Open
Abstract
Background Increasing evidence has indicated that protein-protein interactions (PPIs) play important roles in various aspects of the structural and functional organization of a cell. Thus, continuing to uncover potential PPIs is an important topic in the biomedical domain. Although various feature extraction methods with machine learning approaches have enhanced the prediction of PPIs. There remains room for improvement by developing novel and effective feature extraction methods and classifier approaches to identify PPIs. Method In this study, we proposed a sequence-based feature extraction method called LCPSSMMF, which combined local coding position-specific scoring matrix (PSSM) with multifeatures fusion. First, we used a novel local coding method based on PSSM to build a new PSSM (CPSSM); the advantage of this method is that it incorporated global and local feature extraction, which can account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. Second, we adopted 2 different feature extraction methods (Local Average Group [LAG] and Bigram Probability [BP]) to capture multiple key feature information by employing the evolutionary information embedded in the CPSSM matrix. Finally, feature vectors were acquired by using multifeatures fusion method. Result To evaluate the performance of the proposed feature extraction approach, we employed support vector machine (SVM) as a prediction classifier and applied this method to yeast and human PPI datasets. The prediction accuracies of LCPSSMMF were 93.43% and 90.41% on the yeast and human datasets, respectively. Moreover, we also compared the proposed method with the previous sequence-based approaches on the yeast datasets by using the same SVM classifier. The experimental results indicated that the performance of LCPSSMMF significantly exceeded that of several other state-of-the-art methods. It is proven that the LCPSSMMF approach can capture more local and global discriminatory information than almost all previous methods and can function remarkably well in identifying PPIs. To facilitate extensive research in future proteomics studies, we developed a LCPSSMMFSVM server, which is freely available for academic use at http://219.219.62.123:8888/LCPSSMMFSVM.
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Affiliation(s)
- Ji-Yong An
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.,Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou, People's Republic of China
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.,Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou, People's Republic of China
| | - Yu-Jun Zhao
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.,Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou, People's Republic of China
| | - Zi-Ji Yan
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.,Mine Digitization Engineering Research Center, Ministry of Education, Xuzhou, People's Republic of China
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4
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Inferring Protein-Protein Interaction Networks From Mass Spectrometry-Based Proteomic Approaches: A Mini-Review. Comput Struct Biotechnol J 2019; 17:805-811. [PMID: 31316724 PMCID: PMC6611912 DOI: 10.1016/j.csbj.2019.05.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 05/20/2019] [Accepted: 05/26/2019] [Indexed: 01/06/2023] Open
Abstract
Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.
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5
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Profiling Optimal Conditions for Capturing EDEM Proteins Complexes in Melanoma Using Mass Spectrometry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1140:155-167. [PMID: 31347047 DOI: 10.1007/978-3-030-15950-4_9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Endoplasmic reticulum (ER) resident and secretory proteins that fail to reach their native conformation are selected for degradation through the ER-Associated Degradation (ERAD) pathway. The ER degradation-enhancing alpha-mannosidase-like proteins (EDEMs) were shown to be involved in this pathway but their precise role is still under investigation. Mass spectrometry analysis has contributed significantly to the characterization of protein complexes in the last years. The recent advancements in instrumentation, especially within resolution and speed can provide unique insights concerning the molecular architecture of protein-protein interactions in systems biology. Previous reports have suggested that several protein complexes in ERAD are sensitive to the extraction conditions. Indeed, whilst EDEM proteins can be recovered in most detergents, some of their partners are not solubilized, which further emphasizes the importance of the experimental setup. Here, we define such dynamic interactions of EDEM proteins by employing offline protein fractionation, nanoLC-MS/MS and describe how mass spectrometry can contribute to the characterization of such complexes, particularly within a disease context like melanoma.
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Tian B, Zhao C, Gu F, He Z. A two-step framework for inferring direct protein-protein interaction network from AP-MS data. BMC SYSTEMS BIOLOGY 2017; 11:82. [PMID: 28950876 PMCID: PMC5615237 DOI: 10.1186/s12918-017-0452-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Background Affinity purification-mass spectrometry (AP-MS) has been widely used for generating bait-prey data sets so as to identify underlying protein-protein interactions and protein complexes. However, the AP-MS data sets in terms of bait-prey pairs are highly noisy, where candidate pairs contain many false positives. Recently, numerous computational methods have been developed to identify genuine interactions from AP-MS data sets. However, most of these methods aim at removing false positives that contain contaminants, ignoring the distinction between direct interactions and indirect interactions. Results In this paper, we present an initialization-and-refinement framework for inferring direct PPI networks from AP-MS data, in which an initial network is first generated with existing scoring methods and then a refined network is constructed by the application of indirect association removal methods. Experimental results on several real AP-MS data sets show that our method is capable of identifying more direct interactions than traditional scoring methods. Conclusions The proposed framework is sufficiently general to incorporate any feasible methods in each step so as to have potential for handling different types of AP-MS data in the future applications. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0452-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Bo Tian
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
| | - Can Zhao
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
| | - Feiyang Gu
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China
| | - Zengyou He
- School of Software, Dalian University of Technology, Tuqiang Road, Dalian, China. .,Key Laboratory for Ubiquitous Network and Service Software of Liaoning, Tuqiang Road 321, Dalian, 116600, China.
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7
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Zhang XF, Ou-Yang L, Yan H. Node-based differential network analysis in genomics. Comput Biol Chem 2017; 69:194-201. [DOI: 10.1016/j.compbiolchem.2017.03.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 03/27/2017] [Indexed: 12/26/2022]
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Ou-Yang L, Yan H, Zhang XF. Identifying differential networks based on multi-platform gene expression data. MOLECULAR BIOSYSTEMS 2017; 13:183-192. [PMID: 27868129 DOI: 10.1039/c6mb00619a] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Exploring how the structure of a gene regulatory network differs between two different disease states is fundamental for understanding the biological mechanisms behind disease development and progression. Recently, with rapid advances in microarray technologies, gene expression profiles of the same patients can be collected from multiple microarray platforms. However, previous differential network analysis methods were usually developed based on a single type of platform, which could not utilize the common information shared across different platforms. In this study, we introduce a multi-view differential network analysis model to infer the differential network between two different patient groups based on gene expression profiles collected from multiple platforms. Unlike previous differential network analysis models that need to analyze each platform separately, our model can draw support from multiple data platforms to jointly estimate the differential networks and produce more accurate and reliable results. Our simulation studies demonstrate that our method consistently outperforms other available differential network analysis methods. We also applied our method to identify network rewiring associated with platinum resistance using TCGA ovarian cancer samples. The experimental results demonstrate that the hub genes in our identified differential networks on the PI3K/AKT/mTOR pathway play an important role in drug resistance.
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Affiliation(s)
- Le Ou-Yang
- College of Information Engineering, Shenzhen University, Shenzhen, China and Department of Electronic and Engineering, City University of Hong Kong, Hong Kong, China
| | - Hong Yan
- Department of Electronic and Engineering, City University of Hong Kong, Hong Kong, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, China.
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9
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Differential network analysis from cross-platform gene expression data. Sci Rep 2016; 6:34112. [PMID: 27677586 PMCID: PMC5039701 DOI: 10.1038/srep34112] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 09/07/2016] [Indexed: 01/18/2023] Open
Abstract
Understanding how the structure of gene dependency network changes between two patient-specific groups is an important task for genomic research. Although many computational approaches have been proposed to undertake this task, most of them estimate correlation networks from group-specific gene expression data independently without considering the common structure shared between different groups. In addition, with the development of high-throughput technologies, we can collect gene expression profiles of same patients from multiple platforms. Therefore, inferring differential networks by considering cross-platform gene expression profiles will improve the reliability of network inference. We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks. TDJGL can borrow strength across different patient groups and data platforms to improve the accuracy of estimated networks. Simulation studies demonstrate that TDJGL provides more accurate estimates of gene networks and differential networks than previous competing approaches. We apply TDJGL to the PI3K/AKT/mTOR pathway in ovarian tumors to build differential networks associated with platinum resistance. The hub genes of our inferred differential networks are significantly enriched with known platinum resistance-related genes and include potential platinum resistance-related genes.
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Ou-Yang L, Zhang XF, Dai DQ, Wu MY, Zhu Y, Liu Z, Yan H. Protein complex detection based on partially shared multi-view clustering. BMC Bioinformatics 2016; 17:371. [PMID: 27623844 PMCID: PMC5022186 DOI: 10.1186/s12859-016-1164-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 07/23/2016] [Indexed: 01/05/2023] Open
Abstract
Background Protein complexes are the key molecular entities to perform many essential biological functions. In recent years, high-throughput experimental techniques have generated a large amount of protein interaction data. As a consequence, computational analysis of such data for protein complex detection has received increased attention in the literature. However, most existing works focus on predicting protein complexes from a single type of data, either physical interaction data or co-complex interaction data. These two types of data provide compatible and complementary information, so it is necessary to integrate them to discover the underlying structures and obtain better performance in complex detection. Results In this study, we propose a novel multi-view clustering algorithm, called the Partially Shared Multi-View Clustering model (PSMVC), to carry out such an integrated analysis. Unlike traditional multi-view learning algorithms that focus on mining either consistent or complementary information embedded in the multi-view data, PSMVC can jointly explore the shared and specific information inherent in different views. In our experiments, we compare the complexes detected by PSMVC from single data source with those detected from multiple data sources. We observe that jointly analyzing multi-view data benefits the detection of protein complexes. Furthermore, extensive experiment results demonstrate that PSMVC performs much better than 16 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques. Conclusions In this work, we demonstrate that when integrating multiple data sources, using partially shared multi-view clustering model can help to identify protein complexes which are not readily identifiable by conventional single-view-based methods and other integrative analysis methods. All the results and source codes are available on https://github.com/Oyl-CityU/PSMVC. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1164-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Le Ou-Yang
- College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China.,Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics and Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China
| | - Dao-Qing Dai
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xin Gang Road West, Guangzhou, 510275, China.
| | - Meng-Yun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China
| | - Yuan Zhu
- School of Automation, China University of Geosciences, Wuhan, China
| | - Zhiyong Liu
- Shenzhen Polytechnic, Shenzhen, 518055, China
| | - Hong Yan
- Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
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Zhang XF, Ou-Yang L, Dai DQ, Wu MY, Zhu Y, Yan H. Comparative analysis of housekeeping and tissue-specific driver nodes in human protein interaction networks. BMC Bioinformatics 2016; 17:358. [PMID: 27612563 PMCID: PMC5016887 DOI: 10.1186/s12859-016-1233-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2015] [Accepted: 08/31/2016] [Indexed: 12/31/2022] Open
Abstract
Background Several recent studies have used the Minimum Dominating Set (MDS) model to identify driver nodes, which provide the control of the underlying networks, in protein interaction networks. There may exist multiple MDS configurations in a given network, thus it is difficult to determine which one represents the real set of driver nodes. Because these previous studies only focus on static networks and ignore the contextual information on particular tissues, their findings could be insufficient or even be misleading. Results In this study, we develop a Collective-Influence-corrected Minimum Dominating Set (CI-MDS) model which takes into account the collective influence of proteins. By integrating molecular expression profiles and static protein interactions, 16 tissue-specific networks are established as well. We then apply the CI-MDS model to each tissue-specific network to detect MDS proteins. It generates almost the same MDSs when it is solved using different optimization algorithms. In addition, we classify MDS proteins into Tissue-Specific MDS (TS-MDS) proteins and HouseKeeping MDS (HK-MDS) proteins based on the number of tissues in which they are expressed and identified as MDS proteins. Notably, we find that TS-MDS proteins and HK-MDS proteins have significantly different topological and functional properties. HK-MDS proteins are more central in protein interaction networks, associated with more functions, evolving more slowly and subjected to a greater number of post-translational modifications than TS-MDS proteins. Unlike TS-MDS proteins, HK-MDS proteins significantly correspond to essential genes, ageing genes, virus-targeted proteins, transcription factors and protein kinases. Moreover, we find that besides HK-MDS proteins, many TS-MDS proteins are also linked to disease related genes, suggesting the tissue specificity of human diseases. Furthermore, functional enrichment analysis reveals that HK-MDS proteins carry out universally necessary biological processes and TS-MDS proteins usually involve in tissue-dependent functions. Conclusions Our study uncovers key features of TS-MDS proteins and HK-MDS proteins, and is a step forward towards a better understanding of the controllability of human interactomes. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1233-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xiao-Fei Zhang
- School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China
| | - Le Ou-Yang
- College of Information Engineering, Shenzhen University, Nanhai Ave 3688, Shenzhen, 518060, China
| | - Dao-Qing Dai
- Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China.
| | - Meng-Yun Wu
- School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China
| | - Yuan Zhu
- School of Automation, China University of Geosciences, Lumo Road, Wuhan, 430074, China
| | - Hong Yan
- Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, China
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