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Han S, Liu L. GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs. Comput Struct Biotechnol J 2024; 23:2034-2048. [PMID: 38765609 PMCID: PMC11101938 DOI: 10.1016/j.csbj.2024.04.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 05/22/2024] Open
Abstract
Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.
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Affiliation(s)
- Shuangkai Han
- School of Information, Yunnan Normal University, Kunming, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, China
| | - Lin Liu
- School of Information, Yunnan Normal University, Kunming, China
- Engineering Research Center of Computer Vision and Intelligent Control Technology, Department of Education of Yunnan Province, China
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2
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Yue C, Xue H. Identification and immune landscape of sarcopenia-related molecular clusters in inflammatory bowel disease by machine learning and integrated bioinformatics. Sci Rep 2024; 14:17603. [PMID: 39079987 PMCID: PMC11289443 DOI: 10.1038/s41598-024-68198-w] [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: 03/12/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024] Open
Abstract
Sarcopenia, a prevalent comorbidity of inflammatory bowel disease (IBD), is characterized by diminished skeletal muscle mass and strength. Nevertheless, the underlying interconnected mechanisms remain elusive. This study identified distinct expression patterns of sarcopenia-associated genes (SRGs) across individuals with IBD and in samples of normal tissue. By analyzing SRG expression profiles, we effectively segregated 541 IBD samples into three distinct clusters, each marked by its unique immune landscape. To unravel the transcriptional disruptions underlying these clusters, the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was employed to spotlight key genes linked to each cluster. A diagnostic model based on four key genes (TIMP1, PLAU, PHLDA1, TGFBI) was established using Random Forest and LASSO (least absolute shrinkage and selection operator) algorithms, and validated with the GSE179285 dataset. Moreover, the GSE112366 dataset facilitated the exploration of gene expression dynamics within the ileum mucosa of UC patients pre- and post-Ustekinumab treatment. Additionally, insights into the intricate relationship between immune cells and these pivotal genes were gleaned from the single-cell RNA dataset GSE162335. In conclusion, our findings collectively underscored the pivotal role of sarcopenia-related genes in the pathogenesis of IBD. Their potential as robust biomarkers for future diagnostic and therapeutic strategies is particularly promising, opening avenues for a deeper understanding and improved management of these interconnected conditions.
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Affiliation(s)
- Chongkang Yue
- Department of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 145 Middle Shandong Road, Shanghai, 200001, China
- Department of Gastroenterology, Shanghai Punan Hospital of Pudong New District, Shanghai, China, 200120
| | - Huiping Xue
- Department of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, Renji Hospital, Shanghai Jiao Tong University School of Medicine, 145 Middle Shandong Road, Shanghai, 200001, China.
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3
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Peng L, Ren M, Huang L, Chen M. GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network. Interdiscip Sci 2024; 16:418-438. [PMID: 38733474 DOI: 10.1007/s12539-024-00619-w] [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: 11/18/2023] [Revised: 02/02/2024] [Accepted: 02/03/2024] [Indexed: 05/13/2024]
Abstract
Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.
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Affiliation(s)
- Lihong Peng
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Mengnan Ren
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Liangliang Huang
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, China.
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4
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Zhou L, Peng X, Zeng L, Peng L. Finding potential lncRNA-disease associations using a boosting-based ensemble learning model. Front Genet 2024; 15:1356205. [PMID: 38495672 PMCID: PMC10940470 DOI: 10.3389/fgene.2024.1356205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/01/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
| | - Xinhuai Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
| | - Lijun Zeng
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
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Peng L, Yang Y, Yang C, Li Z, Cheong N. HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4814-4834. [PMID: 38872515 DOI: 10.3934/mbe.2024212] [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: 06/15/2024]
Abstract
Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and therapeutic target. Nevertheless, conventional biological experimental techniques are both resource-intensive and laborious, making it essential to develop an accurate and efficient computational method to facilitate the discovery of potential links between lncRNAs and diseases. In this study, we proposed HRGCNLDA, a computational approach utilizing hierarchical refinement of graph convolutional neural networks for forecasting lncRNA-disease potential associations. This approach effectively addresses the over-smoothing problem that arises from stacking multiple layers of graph convolutional neural networks. Specifically, HRGCNLDA enhances the layer representation during message propagation and node updates, thereby amplifying the contribution of hidden layers that resemble the ego layer while reducing discrepancies. The results of the experiments showed that HRGCNLDA achieved the highest AUC-ROC (area under the receiver operating characteristic curve, AUC for short) and AUC-PR (area under the precision versus recall curve, AUPR for short) values compared to other methods. Finally, to further demonstrate the reliability and efficacy of our approach, we performed case studies on the case of three prevalent human diseases, namely, breast cancer, lung cancer and gastric cancer.
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Affiliation(s)
- Li Peng
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
- Hunan Key Laboratory for Service Computing and Novel Software Technology, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yujie Yang
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Cheng Yang
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang 421002, China
| | - Ngai Cheong
- Faculty of Applied Sciences, Macao Polytechnic University, Macau 999078, China
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Li G, Bai P, Liang C, Luo J. Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction. BMC Genomics 2024; 25:73. [PMID: 38233788 PMCID: PMC10795365 DOI: 10.1186/s12864-024-09998-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/09/2024] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation of gene expression, cell differentiation, and the development of tumors and cancers. Predicting the relationships between lncRNAs and diseases can contribute to a better understanding of the pathogenic mechanisms of disease and provide strong support for the development of advanced treatment methods. RESULTS Therefore, we present an innovative Node-Adaptive Graph Transformer model for predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, we utilize the node-adaptive feature smoothing (NAFS) method to learn the local feature information of nodes and encode the structural information of the fusion similarity network of diseases and lncRNAs using Structural Deep Network Embedding (SDNE). Next, the Transformer module is used to capture potential association information between the network nodes. Finally, we employ a Transformer module with two multi-headed attention layers for learning global-level embedding fusion. Network structure coding is added as the structural inductive bias of the network to compensate for the missing message-passing mechanism in Transformer. NAGTLDA achieved an average AUC of 0.9531 and AUPR of 0.9537 significantly higher than state-of-the-art methods in 5-fold cross validation. We perform case studies on 4 diseases; 55 out of 60 associations between lncRNAs and diseases have been validated in the literatures. The results demonstrate the enormous potential of the graph Transformer structure to incorporate graph structural information for uncovering lncRNA-disease unknown correlations. CONCLUSIONS Our proposed NAGTLDA model can serve as a highly efficient computational method for predicting biological information associations.
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Affiliation(s)
- Guanghui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China.
| | - Peihao Bai
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
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Sheng QJ, Tan Y, Zhang L, Wu ZP, Wang B, He XY. Heterogeneous graph framework for predicting the association between lncRNA and disease and case on uterine fibroid. Comput Biol Med 2023; 165:107331. [PMID: 37619322 DOI: 10.1016/j.compbiomed.2023.107331] [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: 07/13/2023] [Revised: 07/24/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
Long non-coding RNAs (lncRNAs) play crucial regulatory roles in various cellular processes, including gene expression, chromatin remodeling, and protein localization. Dysregulation of lncRNAs has been linked to several diseases, making it essential to understand their functions in disease mechanisms and therapeutic strategies. However, traditional experimental methods for studying lncRNA function are time-consuming, expensive, and offer limited insights. In recent years, computational methods have emerged as valuable tools for predicting lncRNA functions and their associations with diseases. However, many existing methods focus on constructing separate networks for lncRNA and disease similarity, resulting in information loss and insufficient processing capacity for isolated nodes. To address this, we developed 'RGLD' by combining Random Walk with restarting (RWR), Graph Neural Network (GNN), and Graph Attention Networks (GAT) to predict lncRNA-disease associations in a heterogeneous network. RGLD achieved an impressive AUC of 0.88, outperforming other methods. It can also predict novel associations between lncRNAs and diseases. RGLD identified HOTAIR, MEG3, and PVT1 as lncRNAs associated with uterine fibroids. Biological experiments directly or indirectly verified the involvement of these three lncRNAs in uterine fibroids, validating the accuracy of RGLD's predictions. Furthermore, we extensively discussed the functions of the target genes regulated by these lncRNAs in uterine fibroids, providing evidence for their role in the development and progression of the disease.
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Affiliation(s)
- Qing-Jing Sheng
- Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China
| | - Yuan Tan
- Department of Integrated Traditional Chinese Medicine (TCM) & Western Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China
| | - Liyuan Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Zhi-Ping Wu
- Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China
| | - Beiying Wang
- Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China
| | - Xiao-Ying He
- Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tong Ji University, Shanghai, China; Shanghai Key Laboratory of Maternal and Fetal Medicine, Shanghai First Maternity and Infant Hospital, Shanghai, China.
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Sheng N, Wang Y, Huang L, Gao L, Cao Y, Xie X, Fu Y. Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases. Brief Bioinform 2023; 24:bbad276. [PMID: 37529914 DOI: 10.1093/bib/bbad276] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/09/2023] [Accepted: 07/11/2023] [Indexed: 08/03/2023] Open
Abstract
MOTIVATION Identifying the relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating and prognosing diseases. The development of effective computational prediction methods can reduce experimental costs. While numerous methods have been proposed, they often to treat the prediction of lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) as separate task. Models capable of predicting all three relationships simultaneously remain relatively scarce. Our aim is to perform multi-task predictions, which not only construct a unified framework, but also facilitate mutual complementarity of information among lncRNAs, miRNAs and diseases. RESULTS In this work, we propose a novel unsupervised embedding method called graph contrastive learning for multi-task prediction (GCLMTP). Our approach aims to predict LDAs, MDAs and LMIs by simultaneously extracting embedding representations of lncRNAs, miRNAs and diseases. To achieve this, we first construct a triple-layer lncRNA-miRNA-disease heterogeneous graph (LMDHG) that integrates the complex relationships between these entities based on their similarities and correlations. Next, we employ an unsupervised embedding model based on graph contrastive learning to extract potential topological feature of lncRNAs, miRNAs and diseases from the LMDHG. The graph contrastive learning leverages graph convolutional network architectures to maximize the mutual information between patch representations and corresponding high-level summaries of the LMDHG. Subsequently, for the three prediction tasks, multiple classifiers are explored to predict LDA, MDA and LMI scores. Comprehensive experiments are conducted on two datasets (from older and newer versions of the database, respectively). The results show that GCLMTP outperforms other state-of-the-art methods for the disease-related lncRNA and miRNA prediction tasks. Additionally, case studies on two datasets further demonstrate the ability of GCLMTP to accurately discover new associations. To ensure reproducibility of this work, we have made the datasets and source code publicly available at https://github.com/sheng-n/GCLMTP.
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Affiliation(s)
- Nan Sheng
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 130012 Changchun, China
| | - Yan Wang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 130012 Changchun, China
- School of Artificial Intelligence, Jilin University, 130012 Changchun, China
| | - Lan Huang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 130012 Changchun, China
| | - Ling Gao
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 130012 Changchun, China
| | - Yangkun Cao
- School of Artificial Intelligence, Jilin University, 130012 Changchun, China
| | - Xuping Xie
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, 130012 Changchun, China
| | - Yuan Fu
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion, UK
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Sheng N, Huang L, Gao L, Cao Y, Xie X, Wang Y. A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:2810-2826. [PMID: 37030713 DOI: 10.1109/tcbb.2023.3264254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Second, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Third, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods, and we'll keep it updated.
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