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Wang XF, Yu CQ, You ZH, Wang Y, Huang L, Qiao Y, Wang L, Li ZW. BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks. BMC Bioinformatics 2024; 25:264. [PMID: 39127625 DOI: 10.1186/s12859-024-05891-7] [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: 08/10/2023] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
Circular RNA (CircRNA)-microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.
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Affiliation(s)
- Xin-Fei Wang
- School of Information Engineering, Xijing University, Xi'an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
- School of Artificial Intelligence, Jilin University, Changchun, China.
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Yan Qiao
- College of Agriculture and Forestry, Longdong University, Qingyang, China
| | - Lei Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
- Guangxi Academy of Sciences, Nanning, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
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Biyu H, Mengshan L, Yuxin H, Ming Z, Nan W, Lixin G. A miRNA-disease association prediction model based on tree-path global feature extraction and fully connected artificial neural network with multi-head self-attention mechanism. BMC Cancer 2024; 24:683. [PMID: 38840078 PMCID: PMC11151537 DOI: 10.1186/s12885-024-12420-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 05/23/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND MicroRNAs (miRNAs) emerge in various organisms, ranging from viruses to humans, and play crucial regulatory roles within cells, participating in a variety of biological processes. In numerous prediction methods for miRNA-disease associations, the issue of over-dependence on both similarity measurement data and the association matrix still hasn't been improved. In this paper, a miRNA-Disease association prediction model (called TP-MDA) based on tree path global feature extraction and fully connected artificial neural network (FANN) with multi-head self-attention mechanism is proposed. The TP-MDA model utilizes an association tree structure to represent the data relationships, multi-head self-attention mechanism for extracting feature vectors, and fully connected artificial neural network with 5-fold cross-validation for model training. RESULTS The experimental results indicate that the TP-MDA model outperforms the other comparative models, AUC is 0.9714. In the case studies of miRNAs associated with colorectal cancer and lung cancer, among the top 15 miRNAs predicted by the model, 12 in colorectal cancer and 15 in lung cancer were validated respectively, the accuracy is as high as 0.9227. CONCLUSIONS The model proposed in this paper can accurately predict the miRNA-disease association, and can serve as a valuable reference for data mining and association prediction in the fields of life sciences, biology, and disease genetics, among others.
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Affiliation(s)
- Hou Biyu
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Li Mengshan
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China.
| | - Hou Yuxin
- College of Computer Science and Engineering, Shanxi Datong University, Datong, Shanxi, 037000, China
| | - Zeng Ming
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, China
| | - Wang Nan
- College of Life Sciences, Jiaying University, Meizhou, Guangdong, 514000, China
| | - Guan Lixin
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou, Jiangxi, 341000, 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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Ren ZH, Yu CQ, Li LP, You ZH, Li ZW, Zhang SW, Zeng X, Shang YF. SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution. J Chem Inf Model 2024; 64:238-249. [PMID: 38103039 DOI: 10.1021/acs.jcim.3c01665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.
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Affiliation(s)
- Zhong-Hao Ren
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Li-Ping Li
- College of Agriculture and Forestry, Longdong University, Qingyang 745000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Shan-Wen Zhang
- School of Information Engineering, Xijing University, Xi'an 710123, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Yi-Fan Shang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
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