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Yu CQ, Wang XF, Li LP, You ZH, Ren ZH, Chu P, Guo F, Wang ZY. RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding. J Chem Inf Model 2024. [PMID: 39231016 DOI: 10.1021/acs.jcim.4c01118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Abstract
Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https://github.com/1axin/RBNE-CMI.
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Affiliation(s)
- Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123 China
| | - Xin-Fei Wang
- College of Computer Science and Technology, Jilin University, Changchun 130012 China
| | - Li-Ping Li
- Yizhi School of Agriculture and Forestry, Xiangyang Polytechnic Institute, Xianyang 712000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zhong-Hao Ren
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Peng Chu
- School of Information Engineering, Xijing University, Xi'an 710123 China
| | - Feng Guo
- School of Information Engineering, Xijing University, Xi'an 710123 China
| | - Zhen-Yu Wang
- School of Telecommunications, Lanzhou University of Technology, Lanzhou 730000, China
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Zhao YX, Yu CQ, Li LP, Wang DW, Song HF, Wei Y. BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information. Front Genet 2024; 15:1399810. [PMID: 38798699 PMCID: PMC11116695 DOI: 10.3389/fgene.2024.1399810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 04/03/2024] [Indexed: 05/29/2024] Open
Abstract
Increasing research findings suggest that circular RNA (circRNA) exerts a crucial function in the pathogenesis of complex human diseases by binding to miRNA. Identifying their potential interactions is of paramount importance for the diagnosis and treatment of diseases. However, long cycles, small scales, and time-consuming processes characterize previous biological wet experiments. Consequently, the use of an efficient computational model to forecast the interactions between circRNA and miRNA is gradually becoming mainstream. In this study, we present a new prediction model named BJLD-CMI. The model extracts circRNA sequence features and miRNA sequence features by applying Jaccard and Bert's method and organically integrates them to obtain CMI attribute features, and then uses the graph embedding method Line to extract CMI behavioral features based on the known circRNA-miRNA correlation graph information. And then we predict the potential circRNA-miRNA interactions by fusing the multi-angle feature information such as attribute and behavior through Autoencoder in Autoencoder Networks. BJLD-CMI attained 94.95% and 90.69% of the area under the ROC curve on the CMI-9589 and CMI-9905 datasets. When compared with existing models, the results indicate that BJLD-CMI exhibits the best overall competence. During the case study experiment, we conducted a PubMed literature search to confirm that out of the top 10 predicted CMIs, seven pairs did indeed exist. These results suggest that BJLD-CMI is an effective method for predicting interactions between circRNAs and miRNAs. It provides a valuable candidate for biological wet experiments and can reduce the burden of researchers.
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Affiliation(s)
- Yi-Xin Zhao
- School of information Engineering, Xijing University, Xi’an, China
| | - Chang-Qing Yu
- School of information Engineering, Xijing University, Xi’an, China
| | - Li-Ping Li
- School of information Engineering, Xijing University, Xi’an, China
- College of Grassland and Environment Sciences, Xinjiang Agricultural University, Ürümqi, China
| | - Deng-Wu Wang
- School of information Engineering, Xijing University, Xi’an, China
| | - Hui-Fan Song
- School of information Engineering, Xijing University, Xi’an, China
| | - Yu Wei
- School of information Engineering, Xijing University, Xi’an, China
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Chang L, Jin X, Rao Y, Zhang X. Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network. PLANT METHODS 2024; 20:33. [PMID: 38402152 PMCID: PMC10894500 DOI: 10.1186/s13007-024-01158-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 02/14/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND More and more studies show that miRNA plays a crucial role in plants' response to different abiotic stresses. However, traditional experimental methods are often expensive and inefficient, so it is important to develop efficient and economical computational methods. Although researchers have developed machine learning-based method, the information of miRNAs and abiotic stresses has not been fully exploited. Therefore, we propose a novel approach based on graph neural networks for predicting potential miRNA-abiotic stress associations. RESULTS In this study, we fully considered the multi-source feature information from miRNAs and abiotic stresses, and calculated and integrated the similarity network of miRNA and abiotic stress from different feature perspectives using multiple similarity measures. Then, the above multi-source similarity network and association information between miRNAs and abiotic stresses are effectively fused through heterogeneous networks. Subsequently, the Restart Random Walk (RWR) algorithm is employed to extract global structural information from heterogeneous networks, providing feature vectors for miRNA and abiotic stress. After that, we utilized the graph autoencoder based on GIN (Graph Isomorphism Networks) to learn and reconstruct a miRNA-abiotic stress association matrix to obtain potential miRNA-abiotic stress associations. The experimental results show that our model is superior to all known methods in predicting potential miRNA-abiotic stress associations, and the AUPR and AUC metrics of our model achieve 98.24% and 97.43%, respectively, under five-fold cross-validation. CONCLUSIONS The robustness and effectiveness of our proposed model position it as a valuable approach for advancing the field of miRNA-abiotic stress association prediction.
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Affiliation(s)
- Liming Chang
- College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
| | - Xiu Jin
- College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei, 230036, China
| | - Yuan Rao
- College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei, 230036, China
| | - Xiaodan Zhang
- College of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, 230036, China.
- Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment, Anhui Agricultural University, Hefei, 230036, China.
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Guo LX, Wang L, You ZH, Yu CQ, Hu ML, Zhao BW, Li Y. Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations. Brief Bioinform 2024; 25:bbae020. [PMID: 38324624 PMCID: PMC10849193 DOI: 10.1093/bib/bbae020] [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: 10/08/2023] [Revised: 01/01/2024] [Accepted: 01/11/2024] [Indexed: 02/09/2024] Open
Abstract
Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.
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Affiliation(s)
- Lu-Xiang Guo
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Lei Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
- College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China
| | - Chang-Qing Yu
- College of Information Engineering, Xijing University, Xi’an 710123, China
| | - Meng-Lei Hu
- School of Medicine, Peking University, Beijing, 100091, China
| | - Bo-Wei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
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