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Li YC, You ZH, Yu CQ, Wang L, Hu L, Hu PW, Qiao Y, Wang XF, Huang YA. DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information. Brief Funct Genomics 2024; 23:276-285. [PMID: 37539561 DOI: 10.1093/bfgp/elad030] [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: 03/18/2023] [Revised: 05/25/2023] [Accepted: 07/13/2023] [Indexed: 08/05/2023] Open
Abstract
Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.
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Affiliation(s)
- Yue-Chao Li
- School of Information Engineering, Xijing University, Xi'an, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an, China
| | - Lei Wang
- Guangxi Academy of Sciences, Nanning, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Yan Qiao
- College of Agriculture and Forestry, Longdong University, Qingyang 745000, China
| | - Xin-Fei Wang
- School of Information Engineering, Xijing University, Xi'an, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
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Chen Q, Zhang L, Liu Y, Qin Z, Zhao T. PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning. Brief Bioinform 2024; 25:bbae144. [PMID: 38581419 PMCID: PMC10998538 DOI: 10.1093/bib/bbae144] [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: 01/25/2024] [Revised: 02/25/2024] [Accepted: 03/15/2024] [Indexed: 04/08/2024] Open
Abstract
Piwi-interacting RNAs (piRNAs) play a crucial role in various biological processes and are implicated in disease. Consequently, there is an escalating demand for computational tools to predict piRNA-disease interactions. Although there have been computational methods proposed for the detection of piRNA-disease associations, the problem of imbalanced and sparse dataset has brought great challenges to capture the complex relationships between piRNAs and diseases. In response to this necessity, we have developed a novel computational architecture, denoted as PUTransGCN, which uses heterogeneous graph convolutional networks to uncover potential piRNA-disease associations. Additionally, the attention mechanism was used to adjust the weight parameters of aggregation heterogeneous node features automatically. For tackling the imbalanced dataset problem, the combined positive unlabelled learning (PUL) method comprising PU bagging, two-step and spy technique was applied to select reliable negative associations. The features of piRNAs and diseases were derived from three distinct biological sources by PUTransGCN, including information on piRNA sequences, semantic terms related to diseases and the existing network of piRNA-disease associations. In the experiment, PUTransGCN performs in 5-fold cross-validation with an AUC of 0.93 and 0.95 on two datasets, respectively, which outperforms the other six state-of-the-art models. We compared three different PUL methods, and the results of the ablation experiment indicate that the combined PUL method yields the best results. The PUTransGCN could serve as a valuable piRNA-disease prediction tool for upcoming studies in the biomedical field. The code for PUTransGCN is available at https://github.com/chenqiuhao/PUTransGCN.
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Affiliation(s)
- Qiuhao Chen
- Institute of Bioinformatics, Harbin Institute of Technology, 150000, Harbin, Heilongjiang, China
| | - Liyuan Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, 150000, Harbin, Heilongjiang, China
| | - Yaojia Liu
- School of Computer Science and Technology, Harbin Institute of Technology, 150000, Harbin, Heilongjiang, China
| | - Zhonghao Qin
- State Key Laboratory of Robotics and System, Harbin Institute of Technology, 150000, Harbin, Heilongjiang, China
| | - Tianyi Zhao
- School of Computer Science and Technology, Harbin Institute of Technology, 150000, Harbin, Heilongjiang, China
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Guo LX, Wang L, You ZH, Yu CQ, Hu ML, Zhao BW, Li Y. Biolinguistic graph fusion model for circRNA-miRNA association prediction. Brief Bioinform 2024; 25:bbae058. [PMID: 38426324 PMCID: PMC10939421 DOI: 10.1093/bib/bbae058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 01/19/2024] [Accepted: 01/27/2024] [Indexed: 03/02/2024] Open
Abstract
Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.
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Affiliation(s)
- Lu-Xiang Guo
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Lei Wang
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Sciences, Nanning 530007, China
- College of Information Science and Engineering, Zaozhuang University, Shandong 277100, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an, 710129, China
| | - Chang-Qing Yu
- College of Information Engineering, Xijing University, Xi’an 710123, China
| | - Meng-Lei Hu
- School of Medicine, Peking University, Beijing, 100091, China
| | - Bo-Wei Zhao
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China
| | - Yang Li
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
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Gogoshin G, Rodin AS. Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends. Cancers (Basel) 2023; 15:5858. [PMID: 38136405 PMCID: PMC10742144 DOI: 10.3390/cancers15245858] [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: 10/23/2023] [Revised: 12/09/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.
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Affiliation(s)
- Grigoriy Gogoshin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Andrei S. Rodin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
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Meng X, Shang J, Ge D, Yang Y, Zhang T, Liu JX. ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network. BMC Genomics 2023; 24:279. [PMID: 37226081 DOI: 10.1186/s12864-023-09380-8] [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: 03/24/2023] [Accepted: 05/15/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND Piwi-interacting RNAs (piRNAs) have been proven to be closely associated with human diseases. The identification of the potential associations between piRNA and disease is of great significance for complex diseases. Traditional "wet experiment" is time-consuming and high-priced, predicting the piRNA-disease associations by computational methods is of great significance. METHODS In this paper, a method based on the embedding transformation graph convolution network is proposed to predict the piRNA-disease associations, named ETGPDA. Specifically, a heterogeneous network is constructed based on the similarity information of piRNA and disease, as well as the known piRNA-disease associations, which is applied to extract low-dimensional embeddings of piRNA and disease based on graph convolutional network with an attention mechanism. Furthermore, the embedding transformation module is developed for the problem of embedding space inconsistency, which is lightweighter, stronger learning ability and higher accuracy. Finally, the piRNA-disease association score is calculated by the similarity of the piRNA and disease embedding. RESULTS Evaluated by fivefold cross-validation, the AUC of ETGPDA achieves 0.9603, which is better than the other five selected computational models. The case studies based on Head and neck squamous cell carcinoma and Alzheimer's disease further prove the superior performance of ETGPDA. CONCLUSIONS Hence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.
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Affiliation(s)
- Xianghan Meng
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China
| | - Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China.
| | - Daohui Ge
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China.
| | - Yi Yang
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China
| | - Tongdui Zhang
- Science and Technology Innovation Service Institution of Rizhao, Rizhao, 276826, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, Rizhao, 276826, China
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