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He Y, Ning Z, Zhu X, Zhang Y, Liu C, Jiang S, Yuan Z, Zhang H. Plant lncRNA-miRNA Interaction Prediction Based on Counterfactual Heterogeneous Graph Attention Network. Interdiscip Sci 2025; 17:244-256. [PMID: 39382820 DOI: 10.1007/s12539-024-00652-9] [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: 04/03/2024] [Revised: 08/10/2024] [Accepted: 08/12/2024] [Indexed: 10/10/2024]
Abstract
Identifying interactions between long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) provides a new perspective for understanding regulatory relationships in plant life processes. Recently, computational methods based on graph neural networks (GNNs) have been widely employed to predict lncRNA-miRNA interactions (LMIs), which compensate for the inadequacy of biological experiments. However, the low-semantic and noise of graph limit the performance of existing GNN-based methods. In this paper, we develop a novel Counterfactual Heterogeneous Graph Attention Network (CFHAN) to improve the robustness to against the noise and the prediction of plant LMIs. Firstly, we construct a real-world based lncRNA-miRNA (L-M) heterogeneous network. Secondly, CFHAN utilizes the node-level attention, the semantic-level attention, and the counterfactual links to enhance the node embeddings learning. Finally, these embeddings are used as inputs for Multilayer Perceptron (MLP) to predict the interactions between lncRNAs and miRNAs. Evaluating our method on a benchmark dataset of plant LMIs, CFHAN outperforms five state-of-the-art methods, and achieves an average AUC and average ACC of 0.9953 and 0.9733, respectively. This demonstrates CFHAN's ability to predict plant LMIs and exhibits promising cross-species prediction ability, offering valuable insights for experimental LMI researches.
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Affiliation(s)
- Yu He
- College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China
| | - ZiLan Ning
- College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China
| | - XingHui Zhu
- College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China
| | - YinQiong Zhang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China
| | - ChunHai Liu
- Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-Making, College of Plant Protection, Hunan Agricultural University, Changsha, 410128, China
| | - SiWei Jiang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China
| | - ZheMing Yuan
- Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-Making, College of Plant Protection, Hunan Agricultural University, Changsha, 410128, China.
| | - HongYan Zhang
- College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China.
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Xie J, Xu P, Lin Y, Zheng M, Jia J, Tan X, Sun J, Zhao Q. LncRNA-miRNA interactions prediction based on meta-path similarity and Gaussian kernel similarity. J Cell Mol Med 2024; 28:e18590. [PMID: 39347925 PMCID: PMC11441278 DOI: 10.1111/jcmm.18590] [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: 05/04/2024] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 10/01/2024] Open
Abstract
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two typical types of non-coding RNAs that interact and play important regulatory roles in many animal organisms. Exploring the unknown interactions between lncRNAs and miRNAs contributes to a better understanding of their functional involvement. Currently, studying the interactions between lncRNAs and miRNAs heavily relies on laborious biological experiments. Therefore, it is necessary to design a computational method for predicting lncRNA-miRNA interactions. In this work, we propose a method called MPGK-LMI, which utilizes a graph attention network (GAT) to predict lncRNA-miRNA interactions in animals. First, we construct a meta-path similarity matrix based on known lncRNA-miRNA interaction information. Then, we use GAT to aggregate the constructed meta-path similarity matrix and the computed Gaussian kernel similarity matrix to update the feature matrix with neighbourhood information. Finally, a scoring module is used for prediction. By comparing with three state-of-the-art algorithms, MPGK-LMI achieves the best results in terms of performance, with AUC value of 0.9077, AUPR of 0.9327, ACC of 0.9080, F1-score of 0.9143 and precision of 0.8739. These results validate the effectiveness and reliability of MPGK-LMI. Additionally, we conduct detailed case studies to demonstrate the effectiveness and feasibility of our approach in practical applications. Through these empirical results, we gain deeper insights into the functional roles and mechanisms of lncRNA-miRNA interactions, providing significant breakthroughs and advancements in this field of research. In summary, our method not only outperforms others in terms of performance but also establishes its practicality and reliability in biological research through real-case analysis, offering strong support and guidance for future studies and applications.
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Affiliation(s)
- Jingxuan Xie
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Peng Xu
- School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Ye Lin
- College of Computer Science and Technology, Jilin University, Changchun, China
| | - Manyu Zheng
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Jixuan Jia
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China
| | - Xinru Tan
- The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China
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Chen Y, Du Z, Ren X, Pan C, Zhu Y, Li Z, Meng T, Yao X. mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization. Methods 2024; 227:17-26. [PMID: 38705502 DOI: 10.1016/j.ymeth.2024.04.018] [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: 12/22/2023] [Revised: 03/30/2024] [Accepted: 04/28/2024] [Indexed: 05/07/2024] Open
Abstract
Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.
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Affiliation(s)
- Yifan Chen
- Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Zhenya Du
- Guangzhou Xinhua University, 510520, Guangzhou, China
| | - Xuanbai Ren
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
| | - Chu Pan
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
| | - Yangbin Zhu
- Manufacturing and Electronic Engineering, Wenzhou University of Technology, 325027, Wenzhou, China.
| | - Zhen Li
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Tao Meng
- Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao.
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Wei L, Zou Q, Zeng X. Editorial: Artificial intelligence in drug discovery and development. Methods 2024; 226:133-137. [PMID: 38582311 DOI: 10.1016/j.ymeth.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/08/2024] Open
Affiliation(s)
- Leyi Wei
- Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China; School of Software, Shandong University, Jinan 250101, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 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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Xu L, Fu X, Zhuo L, Zhou Z, Liao X, Tian S, Kang R, Chen Y. SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder. Methods 2024; 221:73-81. [PMID: 38123109 DOI: 10.1016/j.ymeth.2023.12.002] [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: 08/30/2023] [Revised: 11/28/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific oral drug delivery. By engineering plants to generate effective, non-toxic miRNA interference sequences, it's possible to broaden their applicability, including the treatment of diseases such as hepatitis C. Consequently, delving into the miRNA-disease associations (MDAs) within herbal medicines holds immense promise for diagnosing and addressing miRNA-related diseases. In our research, we propose the SGAE-MDA model, which harnesses the strengths of a graph autoencoder (GAE) combined with a semi-supervised approach to uncover potential MDAs in herbal medicines more effectively. Leveraging the GAE framework, the SGAE-MDA model exactly integrates the inherent feature vectors of miRNAs and disease nodes with the regulatory data in the miRNA-disease network. Additionally, the proposed semi-supervised learning approach randomly hides the partial structure of the miRNA-disease network, subsequently reconstructing them within the GAE framework. This technique effectively minimizes network noise interference. Through comparison against other leading deep learning models, the results consistently highlighted the superior performance of the proposed SGAE-MDA model. Our code and dataset can be available at: https://github.com/22n9n23/SGAE-MDA.
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Affiliation(s)
- Lei Xu
- Wenzhou University of Technology, Wenzhou, China
| | - Xiangzheng Fu
- Neher's Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao, China; College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
| | - Linlin Zhuo
- Wenzhou University of Technology, Wenzhou, China
| | | | - Xuefeng Liao
- Wenzhou University of Technology, Wenzhou, China.
| | - Sha Tian
- Department of Internal Medicine, College of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China.
| | - Ruofei Kang
- Xuhui Excellent Health Information Technology Co., Ltd., China
| | - Yifan Chen
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China.
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