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Chen Q, Qiu J, Lan W, Cao J. Similarity-guided graph contrastive learning for lncRNA-disease association prediction. J Mol Biol 2024:168609. [PMID: 38750722 DOI: 10.1016/j.jmb.2024.168609] [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: 03/26/2024] [Revised: 04/30/2024] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.
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Affiliation(s)
- Qingfeng Chen
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junlai Qiu
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Wei Lan
- School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
| | - Junyue Cao
- College of Life Science and Technology, Guangxi University, Nanning 530004, Guangxi, China.
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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 2024:10.1007/s12539-024-00619-w. [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] [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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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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Yao D, Zhang B, Li X, Zhan X, Zhan X, Zhang B. Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction. Front Genet 2024; 14:1332273. [PMID: 38264213 PMCID: PMC10803626 DOI: 10.3389/fgene.2023.1332273] [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: 11/02/2023] [Accepted: 12/22/2023] [Indexed: 01/25/2024] Open
Abstract
Increasing evidence indicates that mutations and dysregulation of long non-coding RNA (lncRNA) play a crucial role in the pathogenesis and prognosis of complex human diseases. Computational methods for predicting the association between lncRNAs and diseases have gained increasing attention. However, these methods face two key challenges: obtaining reliable negative samples and incorporating lncRNA-disease association (LDA) information from multiple perspectives. This paper proposes a method called NDMLDA, which combines multi-view feature extraction, unsupervised negative sample denoising, and stacking ensemble classifier. Firstly, an unsupervised method (K-means) is used to design a negative sample denoising module to alleviate the imbalance of samples and the impact of potential noise in the negative samples on model performance. Secondly, graph attention networks are employed to extract multi-view features of both lncRNAs and diseases, thereby enhancing the learning of association information between them. Finally, lncRNA-disease association prediction is implemented through a stacking ensemble classifier. Existing research datasets are integrated to evaluate performance, and 5-fold cross-validation is conducted on this dataset. Experimental results demonstrate that NDMLDA achieves an AUC of 0.9907and an AUPR of 0.9927, with a 5-fold cross-validation variance of less than 0.1%. These results outperform the baseline methods. Additionally, case studies further illustrate the model's potential in cancer diagnosis and precision medicine implementation.
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Affiliation(s)
- Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Bo Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiangkui Li
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaojuan Zhan
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, China
| | - Xiaorong Zhan
- Department of Endocrinology and Metabolism, Hospital of South University of Science and Technology, Shenzhen, China
| | - Binbin Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
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Hu X, Liu D, Zhang J, Fan Y, Ouyang T, Luo Y, Zhang Y, Deng L. A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations. Brief Bioinform 2023; 24:bbad410. [PMID: 37985451 DOI: 10.1093/bib/bbad410] [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: 08/01/2023] [Revised: 10/07/2023] [Accepted: 10/25/2023] [Indexed: 11/22/2023] Open
Abstract
Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.
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Affiliation(s)
- Xiaowen Hu
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Dayun Liu
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Jiaxuan Zhang
- Department of Electrical and Computer Engineering, University of California, San Diego,92093 CA, USA
| | - Yanhao Fan
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Tianxiang Ouyang
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Yue Luo
- School of Computer Science and Engineering, Central South University,410075 Changsha, China
| | - Yuanpeng Zhang
- school of software, Xinjiang University, 830046 Urumqi, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University,410075 Changsha, 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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