1
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Guo C, Wang X, Ren H. Databases and computational methods for the identification of piRNA-related molecules: A survey. Comput Struct Biotechnol J 2024; 23:813-833. [PMID: 38328006 PMCID: PMC10847878 DOI: 10.1016/j.csbj.2024.01.011] [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: 09/11/2023] [Revised: 12/31/2023] [Accepted: 01/15/2024] [Indexed: 02/09/2024] Open
Abstract
Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that plays important roles in many biological processes and major cancer diagnosis and treatment, thus becoming a hot research topic. This study aims to provide an in-depth review of computational piRNA-related research, including databases and computational models. Herein, we perform literature analysis and use comparative evaluation methods to summarize and analyze three aspects of computational piRNA-related research: (i) computational models for piRNA-related molecular identification tasks, (ii) computational models for piRNA-disease association prediction tasks, and (iii) computational resources and evaluation metrics for these tasks. This study shows that computational piRNA-related research has significantly progressed, exhibiting promising performance in recent years, whereas they also suffer from the emerging challenges of inconsistent naming systems and the lack of data. Different from other reviews on piRNA-related identification tasks that focus on the organization of datasets and computational methods, we pay more attention to the analysis of computational models, algorithms, and performances that aim to provide valuable references for computational piRNA-related identification tasks. This study will benefit the theoretical development and practical application of piRNAs by better understanding computational models and resources to investigate the biological functions and clinical implications of piRNA.
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Affiliation(s)
- Chang Guo
- Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510420, China
| | - Xiaoli Wang
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Han Ren
- Laboratory of Language Engineering and Computing, Guangdong University of Foreign Studies, Guangzhou 510420, China
- Laboratory of Language and Artificial Intelligence, Guangdong University of Foreign Studies, Guangzhou 510420, China
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2
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Su Y, Liu J, Wu Q, Gao Z, Wang J, Li H, Zheng C. AMPFLDAP: Adaptive Message Passing and Feature Fusion on Heterogeneous Network for LncRNA-Disease Associations Prediction. Interdiscip Sci 2024; 16:608-622. [PMID: 38581626 DOI: 10.1007/s12539-024-00610-5] [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: 07/31/2023] [Revised: 01/03/2024] [Accepted: 01/03/2024] [Indexed: 04/08/2024]
Abstract
Exploration of the intricate connections between long noncoding RNA (lncRNA) and diseases, referred to as lncRNA-disease associations (LDAs), plays a pivotal and indispensable role in unraveling the underlying molecular mechanisms of diseases and devising practical treatment approaches. It is imperative to employ computational methods for predicting lncRNA-disease associations to circumvent the need for superfluous experimental endeavors. Graph-based learning models have gained substantial popularity in predicting these associations, primarily because of their capacity to leverage node attributes and relationships within the network. Nevertheless, there remains much room for enhancing the performance of these techniques by incorporating and harmonizing the node attributes more effectively. In this context, we introduce a novel model, i.e., Adaptive Message Passing and Feature Fusion (AMPFLDAP), for forecasting lncRNA-disease associations within a heterogeneous network. Firstly, we constructed a heterogeneous network involving lncRNA, microRNA (miRNA), and diseases based on established associations and employing Gaussian interaction profile kernel similarity as a measure. Then, an adaptive topological message passing mechanism is suggested to address the information aggregation for heterogeneous networks. The topological features of nodes in the heterogeneous network were extracted based on the adaptive topological message passing mechanism. Moreover, an attention mechanism is applied to integrate both topological and semantic information to achieve the multimodal features of biomolecules, which are further used to predict potential LDAs. The experimental results demonstrated that the performance of the proposed AMPFLDAP is superior to seven state-of-the-art methods. Furthermore, to validate its efficacy in practical scenarios, we conducted detailed case studies involving three distinct diseases, which conclusively demonstrated AMPFLDAP's effectiveness in the prediction of LDAs.
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Affiliation(s)
- Yansen Su
- Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China.
| | - Jingjing Liu
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, Hefei, 230088, Anhui, China
| | - Qingwen Wu
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, Hefei, 230088, Anhui, China
| | - Zhen Gao
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, Hefei, 230088, Anhui, China
| | - Jing Wang
- Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, 5089 Wangjiang West Road, Hefei, 230088, Anhui, China
| | - Haitao Li
- Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing, Anhui University, 111 Jiulong Road, Hefei, 230601, Anhui, China
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3
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Yao D, Zhang B, Zhan X, Zhang B, Li XK. Predicting lncRNA-Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration. ACS OMEGA 2024; 9:35100-35112. [PMID: 39157140 PMCID: PMC11325412 DOI: 10.1021/acsomega.4c05365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 07/04/2024] [Accepted: 07/22/2024] [Indexed: 08/20/2024]
Abstract
Identifying the associations between long noncoding RNAs (lncRNAs) and disease is critical for disease prevention, diagnosis and treatment. However, conducting wet experiments to discover these associations is time-consuming and costly. Therefore, computational modeling for predicting lncRNA-disease associations (LDAs) has become an important alternative. To enhance the accuracy of LDAs prediction and alleviate the issue of node feature oversmoothing when exploring the potential features of nodes using graph neural networks, we introduce DPFELDA, a dual-path feature extraction network that leverages the integration of information from multiple sources to predict LDA. Initially, we establish a dual-view structure of lncRNAs and disease and a heterogeneous network of lncRNA-disease-microRNA (miRNA) interactions. Subsequently, features are extracted using a dual-path feature extraction network. In particular, we employ a combination of a graph convolutional network, a convolutional block attention module, and a node aggregation layer to perform multilayer topology feature extraction for the dual-view structure of lncRNAs and diseases. Additionally, we utilize a Transformer model to construct the node topology feature residual network for obtaining node-specific features in heterogeneous networks. Finally, XGBoost is employed for LDA prediction. The experimental results demonstrate that DPFELDA outperforms the benchmark model on various benchmark data sets. In the course of model exploration, it becomes evident that DPFELDA successfully alleviates the issue of node feature oversmoothing induced by graph-based learning. Ablation experiments confirm the effectiveness of the innovative module, and a case study substantiates the accuracy of DPFELDA model in predicting novel LDAs for characteristic diseases.
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Affiliation(s)
- Dengju Yao
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
| | - Binbin Zhang
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
| | - Xiaojuan Zhan
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
- College
of Computer Science and Technology, Heilongjiang
Institute of Technology, Harbin 150050, China
| | - Bo Zhang
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
| | - Xiang Kui Li
- School
of Computer Science and Technology, Harbin
University of Science and Technology, Harbin 150080, China
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4
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Xuan P, Lu S, Cui H, Wang S, Nakaguchi T, Zhang T. Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs. J Chem Inf Model 2024; 64:3569-3578. [PMID: 38523267 DOI: 10.1021/acs.jcim.4c00245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- Department of Computer Science, Shantou University, Shantou 515063, China
| | - Siyuan Lu
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne 3083, Australia
| | - Shuai Wang
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 2638522, Japan
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
- School of Mathematical Science, Heilongjiang University, Harbin 150080, China
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5
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Wang S, Qiao J, Feng S. Prediction of lncRNA and disease associations based on residual graph convolutional networks with attention mechanism. Sci Rep 2024; 14:5185. [PMID: 38431702 PMCID: PMC11319593 DOI: 10.1038/s41598-024-55957-y] [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: 11/29/2023] [Accepted: 02/29/2024] [Indexed: 03/05/2024] Open
Abstract
LncRNAs are non-coding RNAs with a length of more than 200 nucleotides. More and more evidence shows that lncRNAs are inextricably linked with diseases. To make up for the shortcomings of traditional methods, researchers began to collect relevant biological data in the database and used bioinformatics prediction tools to predict the associations between lncRNAs and diseases, which greatly improved the efficiency of the study. To improve the prediction accuracy of current methods, we propose a new lncRNA-disease associations prediction method with attention mechanism, called ResGCN-A. Firstly, we integrated lncRNA functional similarity, lncRNA Gaussian interaction profile kernel similarity, disease semantic similarity, and disease Gaussian interaction profile kernel similarity to obtain lncRNA comprehensive similarity and disease comprehensive similarity. Secondly, the residual graph convolutional network was used to extract the local features of lncRNAs and diseases. Thirdly, the new attention mechanism was used to assign the weight of the above features to further obtain the potential features of lncRNAs and diseases. Finally, the training set required by the Extra-Trees classifier was obtained by concatenating potential features, and the potential associations between lncRNAs and diseases were obtained by the trained Extra-Trees classifier. ResGCN-A combines the residual graph convolutional network with the attention mechanism to realize the local and global features fusion of lncRNA and diseases, which is beneficial to obtain more accurate features and improve the prediction accuracy. In the experiment, ResGCN-A was compared with five other methods through 5-fold cross-validation. The results show that the AUC value and AUPR value obtained by ResGCN-A are 0.9916 and 0.9951, which are superior to the other five methods. In addition, case studies and robustness evaluation have shown that ResGCN-A is an effective method for predicting lncRNA-disease associations. The source code for ResGCN-A will be available at https://github.com/Wangxiuxiun/ResGCN-A .
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Affiliation(s)
- Shengchang Wang
- School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Jiaqing Qiao
- School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Shou Feng
- College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, 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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7
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Yao D, Deng Y, Zhan X, Zhan X. Predicting lncRNA-disease associations using multiple metapaths in hierarchical graph attention networks. BMC Bioinformatics 2024; 25:46. [PMID: 38287236 PMCID: PMC11271052 DOI: 10.1186/s12859-024-05672-2] [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: 11/09/2023] [Accepted: 01/23/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Many biological studies have shown that lncRNAs regulate the expression of epigenetically related genes. The study of lncRNAs has helped to deepen our understanding of the pathogenesis of complex diseases at the molecular level. Due to the large number of lncRNAs and the complex and time-consuming nature of biological experiments, applying computer techniques to predict potential lncRNA-disease associations is very effective. To explore information between complex network structures, existing methods rely mainly on lncRNA and disease information. Metapaths have been applied to network models as an effective method for exploring information in heterogeneous graphs. However, existing methods are dominated by lncRNAs or disease nodes and tend to ignore the paths provided by intermediate nodes. METHODS We propose a deep learning model based on hierarchical graphical attention networks to predict unknown lncRNA-disease associations using multiple types of metapaths to extract features. We have named this model the MMHGAN. First, the model constructs a lncRNA-disease-miRNA heterogeneous graph based on known associations and two homogeneous graphs of lncRNAs and diseases. Second, for homogeneous graphs, the features of neighboring nodes are aggregated using a multihead attention mechanism. Third, for the heterogeneous graph, metapaths of different intermediate nodes are selected to construct subgraphs, and the importance of different types of metapaths is calculated and aggregated to obtain the final embedded features. Finally, the features are reconstructed using a fully connected layer to obtain the prediction results. RESULTS We used a fivefold cross-validation method and obtained an average AUC value of 96.07% and an average AUPR value of 93.23%. Additionally, ablation experiments demonstrated the role of homogeneous graphs and different intermediate node path weights. In addition, we studied lung cancer, esophageal carcinoma, and breast cancer. Among the 15 lncRNAs associated with these diseases, 15, 12, and 14 lncRNAs were validated by the lncRNA Disease Database and the Lnc2Cancer Database, respectively. CONCLUSION We compared the MMHGAN model with six existing models with better performance, and the case study demonstrated that the model was effective in predicting the correlation between potential lncRNAs and diseases.
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Affiliation(s)
- Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Yuexiao Deng
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
| | - Xiaojuan Zhan
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, 150050, China
| | - Xiaorong Zhan
- Department of Endocrinology and Metabolism, Hospital of South, University of Science and Technology, Shenzhen, 518055, China
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8
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Lu Z, Zhong H, Tang L, Luo J, Zhou W, Liu L. Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network. PLoS Comput Biol 2023; 19:e1011634. [PMID: 38019786 PMCID: PMC10686445 DOI: 10.1371/journal.pcbi.1011634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
There is a growing body of evidence indicating the crucial roles that long non-coding RNAs (lncRNAs) play in the development and progression of various diseases, including cancers, cardiovascular diseases, and neurological disorders. However, accurately predicting potential lncRNA-disease associations remains a challenge, as existing methods have limitations in extracting heterogeneous association information and handling sparse and unbalanced data. To address these issues, we propose a novel computational method, called HGC-GAN, which combines heterogeneous graph convolutional neural networks (GCN) and generative adversarial networks (GAN) to predict potential lncRNA-disease associations. Specifically, we construct a lncRNA-miRNA-disease heterogeneous network by integrating multiple association data and sequence information. The GCN-based generator is then employed to aggregate neighbor information of nodes and obtain node embeddings, which are used to predict lncRNA-disease associations. Meanwhile, the GAN-based discriminator is trained to distinguish between real and fake lncRNA-disease associations generated by the generator, enabling the generator to improve its ability to generate accurate lncRNA-disease associations gradually. Our experimental results demonstrate that HGC-GAN performs better in predicting potential lncRNA-disease associations, with AUC and AUPR values of 0.9591 and 0.9606, respectively, under 10-fold cross-validation. Moreover, our case study further confirms the effectiveness of HGC-GAN in predicting potential lncRNA-disease associations, even for novel lncRNAs without any known lncRNA-disease associations. Overall, our proposed method HGC-GAN provides a promising approach to predict potential lncRNA-disease associations and may have important implications for disease diagnosis, treatment, and drug development.
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Affiliation(s)
- Zhonghao Lu
- School of Information, Yunnan Normal University, Yunnan, People’s Republic of China
| | - Hua Zhong
- School of Information, Yunnan Normal University, Yunnan, People’s Republic of China
| | - Lin Tang
- Key Laboratory of Educational Information for Nationalities Ministry of Education, Yunnan Normal University, Yunnan, People’s Republic of China
| | - Jing Luo
- State Key Laboratory for Conservation and Utilization of Bio-resource in Yunnan, School of Life Sciences and School of Ecology and Environment, Yunnan University, Kunming, People’s Republic of China
| | - Wei Zhou
- School of Software, Yunnan University, Kunming, People’s Republic of China
| | - Lin Liu
- School of Information, Yunnan Normal University, Yunnan, People’s Republic of 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: 1] [Impact Index Per Article: 1.0] [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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10
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Wang H, Han J, Li H, Duan L, Liu Z, Cheng H. CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:7957-7980. [PMID: 37161181 DOI: 10.3934/mbe.2023345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Circular RNAs (circRNAs) constitute a category of circular non-coding RNA molecules whose abnormal expression is closely associated with the development of diseases. As biological data become abundant, a lot of computational prediction models have been used for circRNA-disease association prediction. However, existing prediction models ignore the non-linear information of circRNAs and diseases when fusing multi-source similarities. In addition, these models fail to take full advantage of the vital feature information of high-similarity neighbor nodes when extracting features of circRNAs or diseases. In this paper, we propose a deep learning model, CDA-SKAG, which introduces a similarity kernel fusion algorithm to integrate multi-source similarity matrices to capture the non-linear information of circRNAs or diseases, and construct a circRNA information space and a disease information space. The model embeds an attention-enhancing layer in the graph autoencoder to enhance the associations between nodes with higher similarity. A cost-sensitive neural network is introduced to address the problem of positive and negative sample imbalance, consequently improving our model's generalization capability. The experimental results show that the prediction performance of our model CDA-SKAG outperformed existing circRNA-disease association prediction models. The results of the case studies on lung and cervical cancer suggest that CDA-SKAG can be utilized as an effective tool to assist in predicting circRNA-disease associations.
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Affiliation(s)
- Huiqing Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Jiale Han
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Haolin Li
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Liguo Duan
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Zhihao Liu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
| | - Hao Cheng
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
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11
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Sheng N, Huang L, Lu Y, Wang H, Yang L, Gao L, Xie X, Fu Y, Wang Y. Data resources and computational methods for lncRNA-disease association prediction. Comput Biol Med 2023; 153:106527. [PMID: 36610216 DOI: 10.1016/j.compbiomed.2022.106527] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/31/2022] [Indexed: 01/03/2023]
Abstract
Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at https://github.com/sheng-n/lncRNA-disease-methods.
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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, Changchun, China
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
| | - Yuting Lu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Hao Wang
- Department of Hepatopancreatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lili Yang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; Department of Obstetrics, The First Hospital of Jilin University, Changchun, China
| | - Ling Gao
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Xuping Xie
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Yuan Fu
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; School of Artificial Intelligence, Jilin University, Changchun, China.
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12
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Zhao X, Wu J, Zhao X, Yin M. Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction. Brief Bioinform 2023; 24:6931723. [PMID: 36528809 DOI: 10.1093/bib/bbac548] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 10/23/2022] [Accepted: 11/11/2022] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity to effectively accelerate experimental screening process of candidate LDAs. However, under the high sparsity of LDA dataset, many computational models hardly exploit enough knowledge to learn comprehensive patterns of node representations. Moreover, although the metapath-based GNN has been recently introduced into LDA prediction, it discards intermediate nodes along the meta-path and results in information loss. RESULTS This paper presents a new multi-view contrastive heterogeneous graph attention network (GAT) for lncRNA-disease association prediction, MCHNLDA for brevity. Specifically, MCHNLDA firstly leverages rich biological data sources of lncRNA, gene and disease to construct two-view graphs, feature structural graph of feature schema view and lncRNA-gene-disease heterogeneous graph of network topology view. Then, we design a cross-contrastive learning task to collaboratively guide graph embeddings of the two views without relying on any labels. In this way, we can pull closer the nodes of similar features and network topology, and push other nodes away. Furthermore, we propose a heterogeneous contextual GAT, where long short-term memory network is incorporated into attention mechanism to effectively capture sequential structure information along the meta-path. Extensive experimental comparisons against several state-of-the-art methods show the effectiveness of proposed framework.The code and data of proposed framework is freely available at https://github.com/zhaoxs686/MCHNLDA.
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Affiliation(s)
- Xiaosa Zhao
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Jun Wu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Xiaowei Zhao
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Minghao Yin
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
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13
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Zhou Y, Wang X, Yao L, Zhu M. LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder. Brief Bioinform 2022; 23:6696138. [PMID: 36094081 DOI: 10.1093/bib/bbac370] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/27/2022] [Accepted: 08/06/2022] [Indexed: 12/14/2022] Open
Abstract
The identification of long noncoding RNA (lncRNA)-disease associations is of great value for disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA-disease associations with computational methods. However, the existing methods do not sufficiently extract key features during data processing, and the learning model parts are either less powerful or overly complex. Therefore, there is still potential to achieve better predictive performance by improving these two aspects. In this work, we propose a novel lncRNA-disease association prediction method LDAformer based on topological feature extraction and Transformer encoder. We construct the heterogeneous network by integrating the associations between lncRNAs, diseases and micro RNAs (miRNAs). Intra-class similarities and inter-class associations are presented as the lncRNA-disease-miRNA weighted adjacency matrix to unify semantics. Next, we design a topological feature extraction process to further obtain multi-hop topological pathway features latent in the adjacency matrix. Finally, to capture the interdependencies between heterogeneous pathways, a Transformer encoder based on the global self-attention mechanism is employed to predict lncRNA-disease associations. The efficient feature extraction and the intuitive and powerful learning model lead to ideal performance. The results of computational experiments on two datasets show that our method outperforms the state-of-the-art baseline methods. Additionally, case studies further indicate its capability to discover new associations accurately.
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Affiliation(s)
- Yi Zhou
- College of Computer Science, Sichuan University, 1st Ring Road South 1 Section, 610065, Chengdu, China
| | - Xinyi Wang
- College of Computer Science, Sichuan University, 1st Ring Road South 1 Section, 610065, Chengdu, China
| | - Lin Yao
- College of Computer Science, Sichuan University, 1st Ring Road South 1 Section, 610065, Chengdu, China
| | - Min Zhu
- College of Computer Science, Sichuan University, 1st Ring Road South 1 Section, 610065, Chengdu, China
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14
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Wu QW, Cao RF, Xia JF, Ni JC, Zheng CH, Su YS. Extra Trees Method for Predicting LncRNA-Disease Association Based On Multi-Layer Graph Embedding Aggregation. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3171-3178. [PMID: 34529571 DOI: 10.1109/tcbb.2021.3113122] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Lots of experimental studies have revealed the significant associations between lncRNAs and diseases. Identifying accurate associations will provide a new perspective for disease therapy. Calculation-based methods have been developed to solve these problems, but these methods have some limitations. In this paper, we proposed an accurate method, named MLGCNET, to discover potential lncRNA-disease associations. Firstly, we reconstructed similarity networks for both lncRNAs and diseases using top k similar information, and constructed a lncRNA-disease heterogeneous network (LDN). Then, we applied Multi-Layer Graph Convolutional Network on LDN to obtain latent feature representations of nodes. Finally, the Extra Trees was used to calculate the probability of association between disease and lncRNA. The results of extensive 5-fold cross-validation experiments show that MLGCNET has superior prediction performance compared to the state-of-the-art methods. Case studies confirm the performance of our model on specific diseases. All the experiment results prove the effectiveness and practicality of MLGCNET in predicting potential lncRNA-disease associations.
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15
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Shi H, Zhang X, Tang L, Liu L. Heterogeneous graph neural network for lncRNA-disease association prediction. Sci Rep 2022; 12:17519. [PMID: 36266433 PMCID: PMC9585029 DOI: 10.1038/s41598-022-22447-y] [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: 05/20/2022] [Accepted: 10/14/2022] [Indexed: 01/12/2023] Open
Abstract
Identifying lncRNA-disease associations is conducive to the diagnosis, treatment and prevention of diseases. Due to the expensive and time-consuming methods verified by biological experiments, prediction methods based on computational models have gradually become an important means of lncRNA-disease associations discovery. However, existing methods still have challenges to make full use of network topology information to identify potential associations between lncRNA and disease in multi-source data. In this study, we propose a novel method called HGNNLDA for lncRNA-disease association prediction. First, HGNNLDA constructs a heterogeneous network composed of lncRNA similarity network, lncRNA-disease association network and lncRNA-miRNA association network; Then, on this heterogeneous network, various types of strong correlation neighbors with fixed size are sampled for each node by restart random walk; Next, the embedding information of lncRNA and disease in each lncRNA-disease association pair is obtained by the method of type-based neighbor aggregation and all types combination though heterogeneous graph neural network, in which attention mechanism is introduced considering that different types of neighbors will make different contributions to the prediction of lncRNA-disease association. As a result, the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUPR) under fivefold cross-validation (5FCV) are 0.9786 and 0.8891, respectively. Compared with five state-of-art prediction models, HGNNLDA has better prediction performance. In addition, in two types of case studies, it is further verified that our method can effectively predict the potential lncRNA-disease associations, and have ability to predict new diseases without any known lncRNAs.
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Affiliation(s)
- Hong Shi
- School of Information, Yunan Normal University, Kunming, 650092 China
| | - Xiaomeng Zhang
- School of Information, Yunan Normal University, Kunming, 650092 China
| | - Lin Tang
- grid.410739.80000 0001 0723 6903Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, 650092 China
| | - Lin Liu
- School of Information, Yunan Normal University, Kunming, 650092 China
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16
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Yao D, Zhang T, Zhan X, Zhang S, Zhan X, Zhang C. Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations. Front Genet 2022; 13:995532. [PMID: 36092871 PMCID: PMC9448985 DOI: 10.3389/fgene.2022.995532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 08/01/2022] [Indexed: 11/20/2022] Open
Abstract
More and more evidences have showed that the unnatural expression of long non-coding RNA (lncRNA) is relevant to varieties of human diseases. Therefore, accurate identification of disease-related lncRNAs can help to understand lncRNA expression at the molecular level and to explore more effective treatments for diseases. Plenty of lncRNA-disease association prediction models have been raised but it is still a challenge to recognize unknown lncRNA-disease associations. In this work, we have proposed a computational model for predicting lncRNA-disease associations based on geometric complement heterogeneous information and random forest. Firstly, geometric complement heterogeneous information was used to integrate lncRNA-miRNA interactions and miRNA-disease associations verified by experiments. Secondly, lncRNA and disease features consisted of their respective similarity coefficients were fused into input feature space. Thirdly, an autoencoder was adopted to project raw high-dimensional features into low-dimension space to learn representation for lncRNAs and diseases. Finally, the low-dimensional lncRNA and disease features were fused into input feature space to train a random forest classifier for lncRNA-disease association prediction. Under five-fold cross-validation, the AUC (area under the receiver operating characteristic curve) is 0.9897 and the AUPR (area under the precision-recall curve) is 0.7040, indicating that the performance of our model is better than several state-of-the-art lncRNA-disease association prediction models. In addition, case studies on colon and stomach cancer indicate that our model has a good ability to predict disease-related lncRNAs.
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Affiliation(s)
- Dengju Yao
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
- *Correspondence: Dengju Yao,
| | - Tao Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaojuan Zhan
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, China
| | - Shuli Zhang
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Xiaorong Zhan
- Department of Endocrinology and Metabolism, Hospital of South University of Science and Technology, Shenzhen, China
| | - Chao Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China
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17
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Xie G, Zhu Y, Lin Z, Sun Y, Gu G, Li J, Wang W. HBRWRLDA: predicting potential lncRNA-disease associations based on hypergraph bi-random walk with restart. Mol Genet Genomics 2022; 297:1215-1228. [PMID: 35752742 DOI: 10.1007/s00438-022-01909-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 05/20/2022] [Indexed: 10/17/2022]
Abstract
Accumulating evidence indicates that the regulation of long non-coding RNAs (lncRNAs) is closely related to a variety of diseases. Identifying meaningful lncRNA-disease associations will help to contribute to the understanding of the molecular mechanisms underlying these diseases. However, only a limited number of associations between lncRNAs and diseases have been inferred from traditional biological experiments due to the high cost and highly specialized. Therefore, computational methods are increasingly used to reduce time of biological experiments and complement biological research. In this paper, a computational method called HBRWRLDA is proposed to predict lncRNA-disease associations. First, HBRWRLDA models the relationships between multiple nodes using hypergraphs, which allows HBRWRLDA to integrate the expression similarity of lncRNAs and the semantic similarity of diseases to construct hypergraphs. Then, a bi-random walk on hypergraphs is used to predict potential lncRNA-disease associations. HBRWRLDA achieves a higher area under the curve value of 0.9551 and [Formula: see text], respectively, compared with the other five advanced methods under the framework of one-leave cross validation (LOOCV) and five-fold cross-validation (5-fold CV). In addition, the prediction effect of HBRWRLDA was confirmed case studies of three diseases: renal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Case studies demonstrates the capacity of HBRWRLDA to identify potentially disease-associated lncRNAs. Overall, HBRWRLDA is excellent at predicting potential lncRNA-disease associations and could be useful in conducting further biological experiments by helping researchers identify candidates of lncRNA-disease association.
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Affiliation(s)
- Guobo Xie
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Yinting Zhu
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhiyi Lin
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Yuping Sun
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Guosheng Gu
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Jianming Li
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
| | - Weiming Wang
- School of Computing, Guangdong University of Technology, Guangzhou, 510000, China
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18
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Liang Y, Zhang ZQ, Liu NN, Wu YN, Gu CL, Wang YL. MAGCNSE: predicting lncRNA-disease associations using multi-view attention graph convolutional network and stacking ensemble model. BMC Bioinformatics 2022; 23:189. [PMID: 35590258 PMCID: PMC9118755 DOI: 10.1186/s12859-022-04715-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/05/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical. RESULTS In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations. CONCLUSIONS The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.
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Affiliation(s)
- Ying Liang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Ze-Qun Zhang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Nian-Nian Liu
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Ya-Nan Wu
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
| | - Chang-Long Gu
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Ying-Long Wang
- College of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, China
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19
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Lan W, Lai D, Chen Q, Wu X, Chen B, Liu J, Wang J, Chen YPP. LDICDL: LncRNA-Disease Association Identification Based on Collaborative Deep Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1715-1723. [PMID: 33125333 DOI: 10.1109/tcbb.2020.3034910] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
It has been proved that long noncoding RNA (lncRNA) plays critical roles in many human diseases. Therefore, inferring associations between lncRNAs and diseases can contribute to disease diagnosis, prognosis and treatment. To overcome the limitation of traditional experimental methods such as expensive and time-consuming, several computational methods have been proposed to predict lncRNA-disease associations by fusing different biological data. However, the prediction performance of lncRNA-disease associations identification needs to be improved. In this study, we propose a computational model (named LDICDL) to identify lncRNA-disease associations based on collaborative deep learning. It uses an automatic encoder to denoise multiple lncRNA feature information and multiple disease feature information, respectively. Then, the matrix decomposition algorithm is employed to predict the potential lncRNA-disease associations. In addition, to overcome the limitation of matrix decomposition, the hybrid model is developed to predict associations between new lncRNA (or disease) and diseases (or lncRNA). The ten-fold cross validation and de novo test are applied to evaluate the performance of method. The experimental results show LDICDL outperforms than other state-of-the-art methods in prediction performance.
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20
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Chen M, Deng Y, Li A, Tan Y. Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network. Front Genet 2022; 13:798632. [PMID: 35186029 PMCID: PMC8854791 DOI: 10.3389/fgene.2022.798632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
Long noncoding RNA (lncRNA), a type of more than 200 nucleotides non-coding RNA, is related to various complex diseases. To precisely identify the potential lncRNA–disease association is important to understand the disease pathogenesis, to develop new drugs, and to design individualized diagnosis and treatment methods for different human diseases. Compared with the complexity and high cost of biological experiments, computational methods can quickly and effectively predict potential lncRNA–disease associations. Thus, it is a promising avenue to develop computational methods for lncRNA-disease prediction. However, owing to the low prediction accuracy ofstate of the art methods, it is vastly challenging to accurately and effectively identify lncRNA-disease at present. This article proposed an integrated method called LPARP, which is based on label-propagation algorithm and random projection to address the issue. Specifically, the label-propagation algorithm is initially used to obtain the estimated scores of lncRNA–disease associations, and then random projections are used to accurately predict disease-related lncRNAs.The empirical experiments showed that LAPRP achieved good prediction on three golddatasets, which is superior to existing state-of-the-art prediction methods. It can also be used to predict isolated diseases and new lncRNAs. Case studies of bladder cancer, esophageal squamous-cell carcinoma, and colorectal cancer further prove the reliability of the method. The proposed LPARP algorithm can predict the potential lncRNA–disease interactions stably and effectively with fewer data. LPARP can be used as an effective and reliable tool for biomedical research.
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21
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Sheng N, Huang L, Wang Y, Zhao J, Xuan P, Gao L, Cao Y. Multi-channel graph attention autoencoders for disease-related lncRNAs prediction. Brief Bioinform 2022; 23:6519791. [PMID: 35108355 DOI: 10.1093/bib/bbab604] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/08/2021] [Accepted: 12/27/2021] [Indexed: 12/31/2022] Open
Abstract
MOTIVATION Predicting disease-related long non-coding RNAs (lncRNAs) can be used as the biomarkers for disease diagnosis and treatment. The development of effective computational prediction approaches to predict lncRNA-disease associations (LDAs) can provide insights into the pathogenesis of complex human diseases and reduce experimental costs. However, few of the existing methods use microRNA (miRNA) information and consider the complex relationship between inter-graph and intra-graph in complex-graph for assisting prediction. RESULTS In this paper, the relationships between the same types of nodes and different types of nodes in complex-graph are introduced. We propose a multi-channel graph attention autoencoder model to predict LDAs, called MGATE. First, an lncRNA-miRNA-disease complex-graph is established based on the similarity and correlation among lncRNA, miRNA and diseases to integrate the complex association among them. Secondly, in order to fully extract the comprehensive information of the nodes, we use graph autoencoder networks to learn multiple representations from complex-graph, inter-graph and intra-graph. Thirdly, a graph-level attention mechanism integration module is adopted to adaptively merge the three representations, and a combined training strategy is performed to optimize the whole model to ensure the complementary and consistency among the multi-graph embedding representations. Finally, multiple classifiers are explored, and Random Forest is used to predict the association score between lncRNA and disease. Experimental results on the public dataset show that the area under receiver operating characteristic curve and area under precision-recall curve of MGATE are 0.964 and 0.413, respectively. MGATE performance significantly outperformed seven state-of-the-art methods. Furthermore, the case studies of three cancers further demonstrate the ability of MGATE to identify potential disease-correlated candidate lncRNAs. The source code and supplementary data are available at https://github.com/sheng-n/MGATE. CONTACT huanglan@jlu.edu.cn, wy6868@jlu.edu.cn.
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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, Changchun 130012, China
| | - Lan Huang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Yan Wang
- Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China.,School of Artificial Intelligence, Jilin University, Changchun 130012, China
| | - Jing Zhao
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus OH 43210, USA
| | - Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Ling Gao
- School of Computer Science and Technology, Heilongjiang University, Harbin 150080, China
| | - Yangkun Cao
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
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22
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Li J, Kong M, Wang D, Yang Z, Hao X. Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network. Front Genet 2022; 12:808962. [PMID: 35058974 PMCID: PMC8763691 DOI: 10.3389/fgene.2021.808962] [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: 11/04/2021] [Accepted: 11/29/2021] [Indexed: 11/24/2022] Open
Abstract
Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.
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Affiliation(s)
- Jianwei Li
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China.,Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China
| | - Mengfan Kong
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Duanyang Wang
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Zhenwu Yang
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Xiaoke Hao
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
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23
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24
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Duan T, Kuang Z, Wang J, Ma Z. GBDTLRL2D Predicts LncRNA-Disease Associations Using MetaGraph2Vec and K-Means Based on Heterogeneous Network. Front Cell Dev Biol 2021; 9:753027. [PMID: 34977011 PMCID: PMC8718797 DOI: 10.3389/fcell.2021.753027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 11/22/2021] [Indexed: 12/16/2022] Open
Abstract
In recent years, the long noncoding RNA (lncRNA) has been shown to be involved in many disease processes. The prediction of the lncRNA-disease association is helpful to clarify the mechanism of disease occurrence and bring some new methods of disease prevention and treatment. The current methods for predicting the potential lncRNA-disease association seldom consider the heterogeneous networks with complex node paths, and these methods have the problem of unbalanced positive and negative samples. To solve this problem, a method based on the Gradient Boosting Decision Tree (GBDT) and logistic regression (LR) to predict the lncRNA-disease association (GBDTLRL2D) is proposed in this paper. MetaGraph2Vec is used for feature learning, and negative sample sets are selected by using K-means clustering. The innovation of the GBDTLRL2D is that the clustering algorithm is used to select a representative negative sample set, and the use of MetaGraph2Vec can better retain the semantic and structural features in heterogeneous networks. The average area under the receiver operating characteristic curve (AUC) values of GBDTLRL2D obtained on the three datasets are 0.98, 0.98, and 0.96 in 10-fold cross-validation.
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Affiliation(s)
| | - Zhufang Kuang
- School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
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25
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Kang C, Zhang H, Liu Z, Huang S, Yin Y. LR-GNN: a graph neural network based on link representation for predicting molecular associations. Brief Bioinform 2021; 23:6456297. [PMID: 34889446 DOI: 10.1093/bib/bbab513] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/03/2021] [Accepted: 11/08/2021] [Indexed: 11/12/2022] Open
Abstract
In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discovering molecular relationships with biological significance. However, it remains a challenge to explore a method that relies on representation learning of links for accurately predicting molecular associations. In this paper, we present a novel GNN based on link representation (LR-GNN) to identify potential molecular associations. LR-GNN applies a graph convolutional network (GCN)-encoder to obtain node embedding. To represent associations between molecules, we design a propagation rule that captures the node embedding of each GCN-encoder layer to construct the LR. Furthermore, the LRs of all layers are fused in output by a designed layer-wise fusing rule, which enables LR-GNN to output more accurate results. Experiments on four biomedical network data, including lncRNA-disease association, miRNA-disease association, protein-protein interaction and drug-drug interaction, show that LR-GNN outperforms state-of-the-art methods and achieves robust performance. Case studies are also presented on two datasets to verify the ability to predict unknown associations. Finally, we validate the effectiveness of the LR by visualization.
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Affiliation(s)
- Chuanze Kang
- College of Artificial Intelligence, Nankai University, Tongyan Road, 300350, Tianjin, China
| | - Han Zhang
- College of Artificial Intelligence, Nankai University, Tongyan Road, 300350, Tianjin, China
| | - Zhuo Liu
- College of Artificial Intelligence, Nankai University, Tongyan Road, 300350, Tianjin, China
| | - Shenwei Huang
- College of Computer Science, Nankai University, Tongyan Road, 300350, Tianjin, China
| | - Yanbin Yin
- Department of Food Science and Technology, University of Nebraska - Lincoln, 1400 R Street, 68588, Nebraska, USA
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Yu H, Shen ZA, Du PF. NPI-RGCNAE: Fast predicting ncRNA-protein interactions using the Relational Graph Convolutional Network Auto-Encoder. IEEE J Biomed Health Inform 2021; 26:1861-1871. [PMID: 34699377 DOI: 10.1109/jbhi.2021.3122527] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
- ncRNAs play important roles in a variety of biological processes by interacting with RNA-binding proteins. Therefore, identifying ncRNA-protein interactions is important to understanding the biological functions of ncRNAs. Since experimental methods to determine ncRNA-protein interactions are always costly and time-consuming, computational methods have been proposed as alternative approaches. We developed a novel method NPI-RGCNAE (predicting ncRNA-Protein Interactions by the Relational Graph Convolutional Network Auto-Encoder). With a reliable negative sample selection strategy, we applied the Relational Graph Convolutional Network encoder and the DistMult decoder to predict ncRNA-protein interactions in an accurate and efficient way. By using the 5-fold cross-validation, we found that our method achieved a comparable performance to all state-of-the-art methods. Our method requires less than 10% training time of all state-of-the-art methods. It is a more efficient choice with large datasets in practice. All datasets and source codes of NPI-RGCNAE have been deposited in a public Github repository (https://github.com/Angelia0hh/NPI-RGCNAE).
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27
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Ghorbani M, Kazi A, Soleymani Baghshah M, Rabiee HR, Navab N. RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data. Med Image Anal 2021; 75:102272. [PMID: 34731774 DOI: 10.1016/j.media.2021.102272] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 10/03/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Abstract
Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis of the rare positive cases (true-positives) among all the patients is vital in a healthcare system. In conventional methods, such imbalance is tackled by assigning appropriate weights to classes in the loss function which is still dependent on the relative values of weights, sensitive to outliers, and in some cases biased towards the minor class(es). In this paper, we propose a Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to prevent the graph-based classifier from emphasizing the samples of any particular class. This is accomplished by associating a graph-based neural network to each class, which is responsible for weighting the class samples and changing the importance of each sample for the classifier. Therefore, the classifier adjusts itself and determines the boundary between classes with more attention to the important samples. The parameters of the classifier and weighting networks are trained by an adversarial approach. We show experiments on synthetic and three publicly available medical datasets. Our results demonstrate the superiority of RA-GCN compared to recent methods in identifying the patient's status on all three datasets. The detailed analysis of our method is provided as quantitative and qualitative experiments on synthetic datasets.
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Affiliation(s)
- Mahsa Ghorbani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran; Computer Aided Medical Procedures, Department of Informatics, Technical University of Munich, Germany.
| | - Anees Kazi
- Computer Aided Medical Procedures, Department of Informatics, Technical University of Munich, Germany
| | | | - Hamid R Rabiee
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
| | - Nassir Navab
- Computer Aided Medical Procedures, Department of Informatics, Technical University of Munich, Germany; Whiting School of Engineering, Johns Hopkins University, Baltimore, USA
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28
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Fan Y, Chen M, Pan X. GCRFLDA: scoring lncRNA-disease associations using graph convolution matrix completion with conditional random field. Brief Bioinform 2021; 23:6363052. [PMID: 34486019 DOI: 10.1093/bib/bbab361] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/19/2021] [Accepted: 08/16/2021] [Indexed: 12/12/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) play important roles in various biological regulatory processes, and are closely related to the occurrence and development of diseases. Identifying lncRNA-disease associations is valuable for revealing the molecular mechanism of diseases and exploring treatment strategies. Thus, it is necessary to computationally predict lncRNA-disease associations as a complementary method for biological experiments. In this study, we proposed a novel prediction method GCRFLDA based on the graph convolutional matrix completion. GCRFLDA first constructed a graph using the available lncRNA-disease association information. Then, it constructed an encoder consisting of conditional random field and attention mechanism to learn efficient embeddings of nodes, and a decoder layer to score lncRNA-disease associations. In GCRFLDA, the Gaussian interaction profile kernels similarity and cosine similarity were fused as side information of lncRNA and disease nodes. Experimental results on four benchmark datasets show that GCRFLDA is superior to other existing methods. Moreover, we conducted case studies on four diseases and observed that 70 of 80 predicted associated lncRNAs were confirmed by the literature.
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Affiliation(s)
- Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology
| | - Meijun Chen
- Guilin University of Electronic Technology, Guilin 541004, China
| | - Xiaoyong Pan
- Department of Automation of Shanghai Jiao Tong University
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29
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DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02675-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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30
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Zhang XM, Liang L, Liu L, Tang MJ. Graph Neural Networks and Their Current Applications in Bioinformatics. Front Genet 2021; 12:690049. [PMID: 34394185 PMCID: PMC8360394 DOI: 10.3389/fgene.2021.690049] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 05/28/2021] [Indexed: 12/22/2022] Open
Abstract
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.
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Affiliation(s)
- Xiao-Meng Zhang
- School of Information, Yunnan Normal University, Kunming, China
| | - Li Liang
- School of Information, Yunnan Normal University, Kunming, China
| | - Lin Liu
- School of Information, Yunnan Normal University, Kunming, China
- Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, China
| | - Ming-Jing Tang
- Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, China
- School of Life Sciences, Yunnan Normal University, Kunming, China
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31
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A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations. BMC Bioinformatics 2021; 22:136. [PMID: 33745450 PMCID: PMC7983260 DOI: 10.1186/s12859-021-04073-z] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 03/11/2021] [Indexed: 01/01/2023] Open
Abstract
Background Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNA-disease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNA-disease associations. The source code and data are available at https://github.com/zhanglabNKU/VGAELDA. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04073-z.
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32
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Ding Y, Lei X, Liao B, Wu FX. Machine learning approaches for predicting biomolecule-disease associations. Brief Funct Genomics 2021; 20:273-287. [PMID: 33554238 DOI: 10.1093/bfgp/elab002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease-biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule-disease prediction methods.
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Affiliation(s)
- Yulian Ding
- Division of Biomedical Engineering at the University of Saskatchewan
| | - Xiujuan Lei
- School of Computer Science at Shaanxi Normal University
| | - Bo Liao
- School of Mathematics and Statistics at Hainan Normal University, Haikou, China
| | - Fang-Xiang Wu
- College of Engineering and the Department of Computer Science at University of Saskatchewan
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Lei X, Mudiyanselage TB, Zhang Y, Bian C, Lan W, Yu N, Pan Y. A comprehensive survey on computational methods of non-coding RNA and disease association prediction. Brief Bioinform 2020; 22:6042241. [PMID: 33341893 DOI: 10.1093/bib/bbaa350] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/20/2020] [Accepted: 11/01/2020] [Indexed: 02/06/2023] Open
Abstract
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.
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Affiliation(s)
- Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | | | - Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Wei Lan
- School of Computer, Electronics and Information at Guangxi University, Nanning, China
| | - Ning Yu
- Department of Computing Sciences at the College at Brockport, State University of New York, Rochester, NY, USA
| | - Yi Pan
- Computer Science Department at Georgia State University, Atlanta, GA, USA
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