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Chen M, Deng Y, Li Z, Ye Y, Zeng L, He Z, Peng G. SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm. J Cell Mol Med 2024; 28:e18345. [PMID: 38693850 PMCID: PMC11063733 DOI: 10.1111/jcmm.18345] [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: 12/31/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024] Open
Abstract
Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.
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Affiliation(s)
- Min Chen
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yingwei Deng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Zejun Li
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yifan Ye
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Lijun Zeng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Ziyi He
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Guofang Peng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
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2
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Liao Q, Ye Y, Li Z, Chen H, Zhuo L. Prediction of miRNA-disease associations in microbes based on graph convolutional networks and autoencoders. Front Microbiol 2023; 14:1170559. [PMID: 37187536 PMCID: PMC10175670 DOI: 10.3389/fmicb.2023.1170559] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 03/21/2023] [Indexed: 05/17/2023] Open
Abstract
MicroRNAs (miRNAs) are short RNA molecular fragments that regulate gene expression by targeting and inhibiting the expression of specific RNAs. Due to the fact that microRNAs affect many diseases in microbial ecology, it is necessary to predict microRNAs' association with diseases at the microbial level. To this end, we propose a novel model, termed as GCNA-MDA, where dual-autoencoder and graph convolutional network (GCN) are integrated to predict miRNA-disease association. The proposed method leverages autoencoders to extract robust representations of miRNAs and diseases and meantime exploits GCN to capture the topological information of miRNA-disease networks. To alleviate the impact of insufficient information for the original data, the association similarity and feature similarity data are combined to calculate a more complete initial basic vector of nodes. The experimental results on the benchmark datasets demonstrate that compared with the existing representative methods, the proposed method has achieved the superior performance and its precision reaches up to 0.8982. These results demonstrate that the proposed method can serve as a tool for exploring miRNA-disease associations in microbial environments.
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Affiliation(s)
- Qingquan Liao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Yuxiang Ye
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
| | - Zihang Li
- School of Computing and Data Science, Xiamen University Malaysia, Sepang, Selangor, Malaysia
| | - Hao Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
- *Correspondence: Hao Chen
| | - Linlin Zhuo
- School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China
- Linlin Zhuo
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Feitosa RM, Prieto-Oliveira P, Brentani H, Machado-Lima A. MicroRNA target prediction tools for animals: Where we are at and where we are going to - A systematic review. Comput Biol Chem 2022; 100:107729. [DOI: 10.1016/j.compbiolchem.2022.107729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 07/08/2022] [Accepted: 07/09/2022] [Indexed: 11/26/2022]
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Yang M, Huang ZA, Gu W, Han K, Pan W, Yang X, Zhu Z. Prediction of biomarker-disease associations based on graph attention network and text representation. Brief Bioinform 2022; 23:6651308. [PMID: 35901464 DOI: 10.1093/bib/bbac298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers. RESULTS Here, we present a novel computational model named GTGenie for predicting the biomarker-disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker-disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker-disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods. AVAILABILITY The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.
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Affiliation(s)
- Minghao Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
| | - Zhi-An Huang
- Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China
| | - Wenhao Gu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China.,GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Kun Han
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Wenying Pan
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Xiao Yang
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
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5
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Toprak A, Eryilmaz Dogan E. Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization. Interdiscip Sci 2021; 13:595-602. [PMID: 34370220 DOI: 10.1007/s12539-021-00469-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/05/2021] [Accepted: 07/30/2021] [Indexed: 10/20/2022]
Abstract
MicroRNA (miRNA) molecules, which are effective in the formation and progression of many different diseases, are 18-22 nucleotides in length and make up a type of non-coding RNA. Predicting disease-related microRNAs is crucial for understanding the pathogenesis of disease and for diagnosis, treatment, and prevention of diseases. Many computational techniques have been studied and developed, as the experimental techniques used to find novel miRNA-disease associations in biology are costly. In this paper, a Kernelized Bayesian Matrix Factorization (KBMF) technique was suggested to predict new relations among miRNAs and diseases with several information such as miRNA functional similarity, disease semantic similarity, and known relations among miRNAs and diseases. AUC value of 0.9450 was obtained by implementing fivefold cross-validation for KBMF technique. We also carried out three kinds of case studies (breast, lung, and colon neoplasms) to prove the performance of KBMF technique, and the predictive reliability of this method was confirmed by the results. Thus, KBMF technique can be used as a reliable computational model to infer possible miRNA-disease associations.
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Affiliation(s)
- Ahmet Toprak
- Department of Electricity and Energy, Bozkır Vocational School, Selcuk University, Bozkır, Konya, Turkey
| | - Esma Eryilmaz Dogan
- Department of Biomedical Engineering, Faculty of Technology, Selcuk University, Selçuklu, Konya, Turkey.
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Toprak A, Eryilmaz E. Prediction of miRNA-disease associations based on Weighted [Formula: see text]-Nearest known neighbors and network consistency projection. J Bioinform Comput Biol 2020; 19:2050041. [PMID: 33148093 DOI: 10.1142/s0219720020500419] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted [Formula: see text]-Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.
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Affiliation(s)
- Ahmet Toprak
- Department of Electricity and Energy, Bozkır Vocational School, Selcuk University, Konya, Turkey
| | - Esma Eryilmaz
- Department of Biomedical Engineering, Faculty of Technology, Selcuk University, Konya, Turkey
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Zhang Y, Chen M, Cheng X, Wei H. MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection. Front Genet 2020; 11:389. [PMID: 32425980 PMCID: PMC7204399 DOI: 10.3389/fgene.2020.00389] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 03/27/2020] [Indexed: 12/11/2022] Open
Abstract
Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.
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Affiliation(s)
- Yi Zhang
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Xiaohui Cheng
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Hanyan Wei
- School of Pharmacy, Guilin Medical University, Guilin, China
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Tang C, Zhou H, Zheng X, Zhang Y, Sha X. Dual Laplacian regularized matrix completion for microRNA-disease associations prediction. RNA Biol 2019; 16:601-611. [PMID: 30676207 PMCID: PMC6546388 DOI: 10.1080/15476286.2019.1570811] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 11/30/2018] [Accepted: 01/03/2019] [Indexed: 01/21/2023] Open
Abstract
Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.
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Affiliation(s)
- Chang Tang
- School of Computer Science, China University of Geosciences, Wuhan, China
| | - Hua Zhou
- Department of Hematology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, China
| | - Xiao Zheng
- Wuhan University of Technology Hospital, Wuhan University of Technology, Wuhan, China
| | - Yanming Zhang
- Department of Hematology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, China
| | - Xiaofeng Sha
- Department of Oncology, Huai’an Hongze District People’s Hospital, Huai’an, China
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Chen M, Zhang Y, Li A, Li Z, Liu W, Chen Z. Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction. Front Genet 2019; 10:385. [PMID: 31080459 PMCID: PMC6497741 DOI: 10.3389/fgene.2019.00385] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 04/10/2019] [Indexed: 12/22/2022] Open
Abstract
In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs.
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Affiliation(s)
- Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Yi Zhang
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Ang Li
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Zejun Li
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Wenhua Liu
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Zheng Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
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Dewanjee S, Bhattacharjee N. MicroRNA: A new generation therapeutic target in diabetic nephropathy. Biochem Pharmacol 2018; 155:32-47. [DOI: 10.1016/j.bcp.2018.06.017] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Accepted: 06/20/2018] [Indexed: 12/11/2022]
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11
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Chen M, Peng Y, Li A, Li Z, Deng Y, Liu W, Liao B, Dai C. A novel information diffusion method based on network consistency for identifying disease related microRNAs. RSC Adv 2018; 8:36675-36690. [PMID: 35558942 PMCID: PMC9088870 DOI: 10.1039/c8ra07519k] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 10/17/2018] [Indexed: 12/27/2022] Open
Abstract
The abnormal expression of miRNAs is directly related to the development of human diseases. Predicting the potential candidate miRNAs associated with diseases can contribute to the detection, diagnosis, treatment and prevention of human complex diseases. The effective inference of the calculation method of the relationship between miRNAs and diseases is an effective supplement to biological experiments. It is of great help in the prevention, treatment and prognosis of complex diseases. This paper proposes a novel information diffusion method based on network consistency (IDNC) for identifying disease related microRNAs. The model first synthesizes the miRNA family information and the miRNA function similarity to reconstruct the miRNA network, and reconstruct the disease network by using the known disease and miRNA-related information and the semantic score between diseases. Then the global similarity of the two networks is obtained by using the Laplacian score of graphs. The global similarity score is a measure of the similarity between diseases and miRNAs. The disease–miRNA relation network was reconstructed by integrating the global similarity relation. The network consistency diffusion seed is then obtained by combining the global similarity network with the reconstructed disease–miRNA association network. Thereafter, the stable diffusion spectrum is generated as the prediction score by using the restarted random walk algorithm. The AUC value obtained by performing the LOOCV in the gold benchmark dataset is 0.8814. The AUC value obtained by performing the LOOCV in the predictive dataset is 0.9512. Compared with other frontier methods, our method has higher accuracy, which is further illustrated by case studies of breast neoplasms and colon neoplasms to prove that IDNC is valuable. The abnormal expression of miRNAs is directly related to the development of human diseases.![]()
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Affiliation(s)
- Min Chen
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
- College of Information Science and Engineering
| | - Yan Peng
- College of International Communication
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Ang Li
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Zejun Li
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
- College of Information Science and Engineering
| | - Yingwei Deng
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Wenhua Liu
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Bo Liao
- College of Information Science and Engineering
- Hunan University
- Changsha 410082
- China
| | - Chengqiu Dai
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
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