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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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2
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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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Wang W, Han P, Li Z, Nie R, Wang K, Wang L, Liao H. LMGATCDA: Graph Neural Network With Labeling Trick for Predicting circRNA-Disease Associations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:289-300. [PMID: 38231821 DOI: 10.1109/tcbb.2024.3355093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Previous studies have proven that circular RNAs (circRNAs) are inextricably connected to the etiology and pathophysiology of complicated diseases. Since conventional biological research are frequently small-scale, expensive, and time-consuming, it is essential to establish an efficient and reasonable computation-based method to identify disease-related circRNAs. In this article, we proposed a novel ensemble model for predicting probable circRNA-disease associations based on multi-source similarity information(LMGATCDA). In particular, LMGATCDA first incorporates information on circRNA functional similarity, disease semantic similarity, and the Gaussian interaction profile (GIP) kernel similarity as explicit features, along with node-labeling of the three-hop subgraphs extracted from each linked target node as graph structural features. After that, the fused features are used as input, and further implied features are extracted by graph sampling aggregation (GraphSAGE) and multi-hop attention graph neural network (MAGNA). Finally, the prediction scores are obtained through a fully connected layer. With five-fold cross-validation, LMGATCDA demonstrated excellent competitiveness against gold standard data, reaching 95.37% accuracy and 91.31% recall with an AUC of 94.25% on the circR2Disease benchmark dataset. Collectively, the noteworthy findings from these case studies support our conclusion that the LMGATCDA model can provide reliable circRNA-disease associations for clinical research while helping to mitigate experimental uncertainties in wet-lab investigations.
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Guo LX, Wang L, You ZH, Yu CQ, Hu ML, Zhao BW, Li Y. Biolinguistic graph fusion model for circRNA-miRNA association prediction. Brief Bioinform 2024; 25:bbae058. [PMID: 38426324 PMCID: PMC10939421 DOI: 10.1093/bib/bbae058] [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: 06/06/2023] [Revised: 01/19/2024] [Accepted: 01/27/2024] [Indexed: 03/02/2024] Open
Abstract
Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.
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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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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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Zhou J, Wang X, Niu R, Shang X, Wen J. Predicting circRNA-miRNA interactions utilizing transformer-based RNA sequential learning and high-order proximity preserved embedding. iScience 2024; 27:108592. [PMID: 38205240 PMCID: PMC10777065 DOI: 10.1016/j.isci.2023.108592] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/20/2023] [Accepted: 11/27/2023] [Indexed: 01/12/2024] Open
Abstract
A key regulatory mechanism involves circular RNA (circRNA) acting as a sponge to modulate microRNA (miRNA), and thus, studying their interaction has significant medical implications. In this field, there are currently two pressing issues that remain unresolved. Firstly, due to the scarcity of verified interactions, we require a minimal amount of samples for training. Secondly, the current models lack interpretability. Therefore, we propose SPBCMI, a method that combines sequence features extracted using the Bidirectional Encoder Representations from Transformer (BERT) model and structural features of biological molecule networks extracted through graph embedding to train a GBDT (Gradient-boosted decision trees) classifier for prediction. Our method yielded an AUC of 0.9143, which is currently the best for this problem. Furthermore, in the case study, SPBCMI accurately predicted 7 out of 10 circRNA-miRNA interactions. These results show that our method provides an innovative and high-performing approach to understanding the interaction between circRNA and miRNA.
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Affiliation(s)
- Jiren Zhou
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
- The John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2601, Australia
| | - Xinfei Wang
- School of Information Engineering, Xijing University, Xi’an, China
| | - Rui Niu
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
- The John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2601, Australia
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Jiayu Wen
- The John Curtin School of Medical Research, The Australian National University, Canberra, ACT 2601, Australia
- Australian Research Council Centre of Excellence for the Mathematical Analysis of Cellular Systems
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Wang XF, Yu CQ, You ZH, Qiao Y, Li ZW, Huang WZ. An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding. Comput Biol Med 2023; 165:107421. [PMID: 37672925 DOI: 10.1016/j.compbiomed.2023.107421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 07/10/2023] [Accepted: 08/28/2023] [Indexed: 09/08/2023]
Abstract
MOTIVATION Accumulating clinical evidence shows that circular RNA (circRNA) plays an important regulatory role in the occurrence and development of human diseases, which is expected to provide a new perspective for the diagnosis and treatment of related diseases. Using computational methods can provide high probability preselection for wet experiments to save resources. However, due to the lack of neighborhood structure in sparse biological networks, the model based on network embedding and graph embedding is difficult to achieve ideal results. RESULTS In this paper, we propose BioDGW-CMI, which combines biological text mining and wavelet diffusion-based sparse network structure embedding to predict circRNA-miRNA interaction (CMI). In detail, BioDGW-CMI first uses the Bidirectional Encoder Representations from Transformers (BERT) for biological text mining to mine hidden features in RNA sequences, then constructs a CMI network, obtains the topological structure embedding of nodes in the network through heat wavelet diffusion patterns. Next, the Denoising autoencoder organically combines the structural features and Gaussian kernel similarity, finally, the feature is sent to lightGBM for training and prediction. BioDGW-CMI achieves the highest prediction performance in all three datasets in the field of CMI prediction. In the case study, all the 8 pairs of CMI based on circ-ITCH were successfully predicted. AVAILABILITY The data and source code can be found at https://github.com/1axin/BioDGW-CMI-model.
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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 Qiao
- College of Agriculture and Forestry, Longdong University, Qingyang, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Wen-Zhun Huang
- School of Information Engineering, Xijing University, Xi'an, China
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Wei MM, Yu CQ, Li LP, You ZH, Wang L. BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path. J Chem Inf Model 2023; 63:5384-5394. [PMID: 37535872 DOI: 10.1021/acs.jcim.3c00852] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
More and more evidence suggests that circRNA plays a vital role in generating and treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA-miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming and costly, and the results will be affected by objective factors. In this paper, we propose a computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes the bidirectional encoding capability of the BERT algorithm to extract sequence features from the semantic information of circRNA and miRNA. Then, a heterogeneous network is constructed based on cosine similarity and known CMI information. The Metapath2vec is employed to conduct random walks following meta-paths in the network to capture topological features, including similarity features. Finally, potential CMIs are predicted using the XGBoost classifier. BCMCMI achieves superior results compared to other state-of-the-art models on two benchmark datasets for CMI prediction. We also utilize t-SNE to visually observe the distribution of the extracted features on a randomly selected dataset. The remarkable prediction results show that BCMCMI can serve as a valuable complement to the wet experiment process.
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Affiliation(s)
- Meng-Meng Wei
- School of Information Engineering, Xijing University, Xi'an, Shaanxi 710123, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an, Shaanxi 710123, China
| | - Li-Ping Li
- College of Agriculture and Forestry, Longdong University, Qingyang, Gansu 745000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Lei Wang
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, Guangxi 530007, China
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Wang XF, Yu CQ, You ZH, Qiao Y, Li ZW, Huang WZ, Zhou JR, Jin HY. KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder. iScience 2023; 26:107478. [PMID: 37583550 PMCID: PMC10424127 DOI: 10.1016/j.isci.2023.107478] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/16/2023] [Accepted: 07/21/2023] [Indexed: 08/17/2023] Open
Abstract
Circular RNA (circRNA) plays an important role in the diagnosis, treatment, and prognosis of human diseases. The discovery of potential circRNA-miRNA interactions (CMI) is of guiding significance for subsequent biological experiments. Limited by the small amount of experimentally supported data and high randomness, existing models are difficult to accomplish the CMI prediction task based on real cases. In this paper, we propose KS-CMI, a novel method for effectively accomplishing CMI prediction in real cases. KS-CMI enriches the 'behavior relationships' of molecules by constructing circRNA-miRNA-cancer (CMCI) networks and extracts the behavior relationship attribute of molecules based on balance theory. Next, the denoising autoencoder (DAE) is used to enhance the feature representation of molecules. Finally, the CatBoost classifier was used for prediction. KS-CMI achieved the most reliable prediction results in real cases and achieved competitive performance in all datasets in the CMI prediction.
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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 Qiao
- College of Agriculture and Forestry, Longdong University, Qingyang, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China
| | - Wen-Zhun Huang
- School of Information Engineering, Xijing University, Xi’an, China
| | - Ji-Ren Zhou
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Hai-Yan Jin
- School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China
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Wei MM, Yu CQ, Li LP, You ZH, Ren ZH, Guan YJ, Wang XF, Li YC. LPIH2V: LncRNA-protein interactions prediction using HIN2Vec based on heterogeneous networks model. Front Genet 2023; 14:1122909. [PMID: 36845392 PMCID: PMC9950107 DOI: 10.3389/fgene.2023.1122909] [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: 12/13/2022] [Accepted: 01/30/2023] [Indexed: 02/12/2023] Open
Abstract
LncRNA-protein interaction plays an important role in the development and treatment of many human diseases. As the experimental approaches to determine lncRNA-protein interactions are expensive and time-consuming, considering that there are few calculation methods, therefore, it is urgent to develop efficient and accurate methods to predict lncRNA-protein interactions. In this work, a model for heterogeneous network embedding based on meta-path, namely LPIH2V, is proposed. The heterogeneous network is composed of lncRNA similarity networks, protein similarity networks, and known lncRNA-protein interaction networks. The behavioral features are extracted in a heterogeneous network using the HIN2Vec method of network embedding. The results showed that LPIH2V obtains an AUC of 0.97 and ACC of 0.95 in the 5-fold cross-validation test. The model successfully showed superiority and good generalization ability. Compared to other models, LPIH2V not only extracts attribute characteristics by similarity, but also acquires behavior properties by meta-path wandering in heterogeneous networks. LPIH2V would be beneficial in forecasting interactions between lncRNA and protein.
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Affiliation(s)
- Meng-Meng Wei
- School of Information Engineering, Xijing University, Xi’an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi’an, China,*Correspondence: Chang-Qing Yu, ; Li-Ping Li,
| | - Li-Ping Li
- School of Information Engineering, Xijing University, Xi’an, China,College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, China,*Correspondence: Chang-Qing Yu, ; Li-Ping Li,
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Zhong-Hao Ren
- School of Information Engineering, Xijing University, Xi’an, China
| | - Yong-Jian Guan
- School of Information Engineering, Xijing University, Xi’an, China
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Yao D, Nong L, Qin M, Wu S, Yao S. Identifying circRNA-miRNA interaction based on multi-biological interaction fusion. Front Microbiol 2022; 13:987930. [PMID: 36620017 PMCID: PMC9815023 DOI: 10.3389/fmicb.2022.987930] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
CircRNA is a new type of non-coding RNA with a closed loop structure. More and more biological experiments show that circRNA plays important roles in many diseases by regulating the target genes of miRNA. Therefore, correct identification of the potential interaction between circRNA and miRNA not only helps to understand the mechanism of the disease, but also contributes to the diagnosis, treatment, and prognosis of the disease. In this study, we propose a model (IIMCCMA) by using network embedding and matrix completion to predict the potential interaction of circRNA-miRNA. Firstly, the corresponding adjacency matrix is constructed based on the experimentally verified circRNA-miRNA interaction, circRNA-cancer interaction, and miRNA-cancer interaction. Then, the Gaussian kernel function and the cosine function are used to calculate the circRNA Gaussian interaction profile kernel similarity, circRNA functional similarity, miRNA Gaussian interaction profile kernel similarity, and miRNA functional similarity. In order to reduce the influence of noise and redundant information in known interactions, this model uses network embedding to extract the potential feature vectors of circRNA and miRNA, respectively. Finally, an improved inductive matrix completion algorithm based on the feature vectors of circRNA and miRNA is used to identify potential interactions between circRNAs and miRNAs. The 10-fold cross-validation experiment is utilized to prove the predictive ability of the IIMCCMA. The experimental results show that the AUC value and AUPR value of the IIMCCMA model are higher than other state-of-the-art algorithms. In addition, case studies show that the IIMCCMA model can correctly identify the potential interactions between circRNAs and miRNAs.
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Affiliation(s)
- Dunwei Yao
- Department of Gastroenterology, The People’s Hospital of Baise, Baise, China,The Southwest Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Lidan Nong
- Department of Child Healthcare, Baise Maternal and Child Hospital, Baise, China
| | - Minzhen Qin
- Department of Gastroenterology, The People’s Hospital of Baise, Baise, China,The Southwest Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Shengbin Wu
- The Southwest Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China,Department of Pulmonary and Critical Care Medicine, The People's Hospital of Baise, Baise, China
| | - Shunhan Yao
- Medical College of Guangxi University, Nanning, China,*Correspondence: Shunhan Yao,
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