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Sheng N, Xie X, Wang Y, Huang L, Zhang S, Gao L, Wang H. A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:328-347. [PMID: 38194377 DOI: 10.1109/tcbb.2024.3351752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.
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2
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Zhang Y, Li X. Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery. J Chem Inf Model 2024; 64:3537-3547. [PMID: 38523272 DOI: 10.1021/acs.jcim.3c01973] [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
Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, significantly impacting treatment efficacy. Extensive research has exposed the intimate involvement of noncoding RNAs (ncRNAs) in conferring resistance to cancer drugs. Understanding the intricate associations between ncRNAs and drug resistance is of pivotal importance in advancing clinical interventions and expediting drug development. However, traditional biological experimental methods are hampered by limitations, such as labor intensiveness, time consumption, and constraints in scalability. Addressing these challenges necessitates the development of efficient computational methods for the accurate prediction of potential ncRNA-drug resistance associations (NDRA). However, most existing predictive models primarily focus on known ncRNA-drug resistance associations, often neglecting the critical aspect of similarity information between ncRNAs and drug resistance. This oversight may hinder the accuracy of characterizing these associations. To overcome the limitations of existing computational models, we proposed B-NDRA, a computational framework designed for the discovery of drug resistance-related ncRNA. Initially, we constructed a heterogeneous graph that integrates ncRNA-drug resistance pairs, leveraging both known associations and similarity fusion information between ncRNAs and drug resistance. Subsequently, we employed an attention mechanism to aggregate local features of graph nodes following a dimensionality reduction of node features. Further, a graph neural network (GNN) facilitated the learning of global node embeddings. Notably, the integration of dual adaptive deep adjustment architectures, encompassing intrablock and interblock methodologies, enabled efficient extraction of global features while balancing local and global features. Finally, B-NDRA employed a multilayer perceptron to predict associations between ncRNAs and drug resistance. Through rigorous 5-fold cross-validation, B-NDRA achieved average AUC, AUPR, Accuracy, Precision, Recall, and F1-score values of 92.2%, 91.9%, 84.88%, 86.9%, 82.37%, and 84.44%, respectively. Furthermore, comparative evaluations were conducted on established models, namely, GAEMDA, GRPAMDA, and LRGCPND. The results, obtained through three distinct 5-fold cross-validation strategies, demonstrated a notable performance improvement across almost all metrics for our B-NDRA. Specific case studies targeting Doxorubicin and Imatinib further validated the practicality of our B-NDRA in discovering potential NDRA. These results confirm the potential of our B-NDRA as a valuable tool in advancing cancer research and therapeutic development. The source code and data set of B-NDRA can be found at https://github.com/XuanLi1145/B-NDRA.
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Affiliation(s)
- Yi Zhang
- Guilin University of Technology, Guilin 541004, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China
| | - Xuanzhao Li
- Guilin University of Technology, Guilin 541004, China
- Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China
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3
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Fan Y, Zhang C, Hu X, Huang Z, Xue J, Deng L. SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning. Brief Bioinform 2024; 25:bbae231. [PMID: 38754409 PMCID: PMC11097980 DOI: 10.1093/bib/bbae231] [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: 01/31/2024] [Revised: 04/15/2024] [Accepted: 04/30/2024] [Indexed: 05/18/2024] Open
Abstract
Drug repurposing offers a viable strategy for discovering new drugs and therapeutic targets through the analysis of drug-gene interactions. However, traditional experimental methods are plagued by their costliness and inefficiency. Despite graph convolutional network (GCN)-based models' state-of-the-art performance in prediction, their reliance on supervised learning makes them vulnerable to data sparsity, a common challenge in drug discovery, further complicating model development. In this study, we propose SGCLDGA, a novel computational model leveraging graph neural networks and contrastive learning to predict unknown drug-gene associations. SGCLDGA employs GCNs to extract vector representations of drugs and genes from the original bipartite graph. Subsequently, singular value decomposition (SVD) is employed to enhance the graph and generate multiple views. The model performs contrastive learning across these views, optimizing vector representations through a contrastive loss function to better distinguish positive and negative samples. The final step involves utilizing inner product calculations to determine association scores between drugs and genes. Experimental results on the DGIdb4.0 dataset demonstrate SGCLDGA's superior performance compared with six state-of-the-art methods. Ablation studies and case analyses validate the significance of contrastive learning and SVD, highlighting SGCLDGA's potential in discovering new drug-gene associations. The code and dataset for SGCLDGA are freely available at https://github.com/one-melon/SGCLDGA.
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Affiliation(s)
- Yanhao Fan
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Che Zhang
- School of software, Xinjiang University, 830046, Urumqi, China
| | - Xiaowen Hu
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Zhijian Huang
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Jiameng Xue
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
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4
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Xie GB, Yu JR, Lin ZY, Gu GS, Chen RB, Xu HJ, Liu ZG. Prediction of miRNA-disease associations based on strengthened hypergraph convolutional autoencoder. Comput Biol Chem 2024; 108:107992. [PMID: 38056378 DOI: 10.1016/j.compbiolchem.2023.107992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 11/04/2023] [Accepted: 11/24/2023] [Indexed: 12/08/2023]
Abstract
Most existing graph neural network-based methods for predicting miRNA-disease associations rely on initial association matrices to pass messages, but the sparsity of these matrices greatly limits performance. To address this issue and predict potential associations between miRNAs and diseases, we propose a method called strengthened hypergraph convolutional autoencoder (SHGAE). SHGAE leverages multiple layers of strengthened hypergraph neural networks (SHGNN) to obtain robust node embeddings. Within SHGNN, we design a strengthened hypergraph convolutional network module (SHGCN) that enhances original graph associations and reduces matrix sparsity. Additionally, SHGCN expands node receptive fields by utilizing hyperedge features as intermediaries to obtain high-order neighbor embeddings. To improve performance, we also incorporate attention-based fusion of self-embeddings and SHGCN embeddings. SHGAE predicts potential miRNA-disease associations using a multilayer perceptron as the decoder. Across multiple metrics, SHGAE outperforms other state-of-the-art methods in five-fold cross-validation. Furthermore, we evaluate SHGAE on colon and lung neoplasms cases to demonstrate its ability to predict potential associations. Notably, SHGAE also performs well in the analysis of gastric neoplasms without miRNA associations.
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Affiliation(s)
- Guo-Bo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Jun-Rui Yu
- School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Zhi-Yi Lin
- School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Guo-Sheng Gu
- School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Rui-Bin Chen
- School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Hao-Jie Xu
- School of Computer Science, Guangdong University of Technology, Guangzhou, 510000, China.
| | - Zhen-Guo Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China.
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5
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Hu H, Zhao H, Zhong T, Dong X, Wang L, Han P, Li Z. Adaptive deep propagation graph neural network for predicting miRNA-disease associations. Brief Funct Genomics 2023; 22:453-462. [PMID: 37078739 DOI: 10.1093/bfgp/elad010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 02/13/2023] [Accepted: 03/09/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA-disease associations. RESULTS In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA-disease associations (ADPMDA). We first construct the miRNA-disease heterogeneous graph based on known miRNA-disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA-disease pairs. CONCLUSION Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA-disease associations.
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Affiliation(s)
- Hua Hu
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
| | - Huan Zhao
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China
| | - Tangbo Zhong
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221008, China
| | - Xishang Dong
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
| | - Lei Wang
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning 541006, China
| | - Pengyong Han
- Central Lab, Changzhi Medical College, Changzhi 046012, China
| | - Zhengwei Li
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277122, China
- Big Data and Intelligent Computing Research Center, Guangxi Academy of Science, Nanning 541006, China
- KUNPAND Communications (Kunshan) Co., Ltd., Suzhou 215300, China
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Wang S, Wang F, Qiao S, Zhuang Y, Zhang K, Pang S, Nowak R, Lv Z. MSHGANMDA: Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction. IEEE J Biomed Health Inform 2023; 27:4639-4648. [PMID: 35759606 DOI: 10.1109/jbhi.2022.3186534] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have identified multi-types of associations between miRNAs and diseases, existing computational methods are unable to sufficiently mine the knowledge in these associations to predict unknown associations. In this study, we innovatively propose a heterogeneous graph attention network model based on meta-subgraphs (MSHGANMDA) to predict the potential miRNA-disease associations. Firstly, we define five types of meta-subgraph from the known miRNA-disease associations. Then, we use meta-subgraph attention and meta-subgraph semantic attention to extract features of miRNA-disease pairs within and between these five meta-subgraphs, respectively. Finally, we apply a fully-connected layer (FCL) to predict the scores of unknown miRNA-disease associations and cross-entropy loss to train our model end-to-end. To evaluate the effectiveness of MSHGANMDA, we apply five-fold cross-validation to calculate the mean values of evaluation metrics Accuracy, Precision, Recall, and F1-score as 0.8595, 0.8601, 0.8596, and 0.8595, respectively. Experiments show that our model, which primarily utilizes multi-types of miRNA-disease association data, gets the greatest ROC-AUC value of 0.934 when compared to other state-of-the-art approaches. Furthermore, through case studies, we further confirm the effectiveness of MSHGANMDA in predicting unknown diseases.
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Gu C, Li X. Prediction of disease-related miRNAs by voting with multiple classifiers. BMC Bioinformatics 2023; 24:177. [PMID: 37122001 PMCID: PMC10150488 DOI: 10.1186/s12859-023-05308-x] [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: 12/31/2022] [Accepted: 04/26/2023] [Indexed: 05/02/2023] Open
Abstract
There is strong evidence to support that mutations and dysregulation of miRNAs are associated with a variety of diseases, including cancer. However, the experimental methods used to identify disease-related miRNAs are expensive and time-consuming. Effective computational approaches to identify disease-related miRNAs are in high demand and would aid in the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In this study, we develop an ensemble learning framework to reveal the potential associations between miRNAs and diseases (ELMDA). The ELMDA framework does not rely on the known associations when calculating miRNA and disease similarities and uses multi-classifiers voting to predict disease-related miRNAs. As a result, the average AUC of the ELMDA framework was 0.9229 for the HMDD v2.0 database in a fivefold cross-validation. All potential associations in the HMDD V2.0 database were predicted, and 90% of the top 50 results were verified with the updated HMDD V3.2 database. The ELMDA framework was implemented to investigate gastric neoplasms, prostate neoplasms and colon neoplasms, and 100%, 94%, and 90%, respectively, of the top 50 potential miRNAs were validated by the HMDD V3.2 database. Moreover, the ELMDA framework can predict isolated disease-related miRNAs. In conclusion, ELMDA appears to be a reliable method to uncover disease-associated miRNAs.
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Affiliation(s)
- Changlong Gu
- College of Information Science and Engineering, Hunan University, Changsha, 410082, Hunan, China.
| | - Xiaoying Li
- College of Information Science and Engineering, Hunan University, Changsha, 410082, Hunan, China.
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8
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Luo Y, Peng L, Shan W, Sun M, Luo L, Liang W. Machine learning in the development of targeting microRNAs in human disease. Front Genet 2023; 13:1088189. [PMID: 36685965 PMCID: PMC9845262 DOI: 10.3389/fgene.2022.1088189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/12/2022] [Indexed: 01/05/2023] Open
Abstract
A microRNA is a small, single-stranded, non-coding ribonucleic acid that plays a crucial role in RNA silencing and can regulate gene expression. With the in-depth study of miRNA in development and disease, miRNA has become an attractive target for novel therapeutic strategies. Exploring miRNA targeting therapy only through experiments is expensive and laborious, so it is essential to develop novel and efficient computational methods to narrow down the search. Recent advances in machine learning applied in biomedical informatics provide opportunities to explore miRNA-targeting drugs, thus promoting miRNA therapeutics. This review provides an overview of recent advancements in miRNA targeting therapeutic using machine learning. First, we mainly describe the basics of predicting miRNA targeting drugs, including pharmacogenomic data resources and data preprocessing. Then we present primary machine learning algorithms and elaborate their application in discovering relationships among miRNAs, drugs, and diseases. Along with the progress of miRNA targeting therapeutics, we finally analyze and discuss the current challenges and opportunities that machine learning confronts.
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Affiliation(s)
- Yuxun Luo
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China,Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China,Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China
| | - Wenyu Shan
- School of Computer Science, University of South China, Hengyang, China
| | - Mengyue Sun
- School of Polymer Science and Polymer Engineering, The University of Akron, Akron, OH, United States
| | - Lingyun Luo
- School of Computer Science, University of South China, Hengyang, China
| | - Wei Liang
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China,Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China,*Correspondence: Wei Liang,
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9
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Pang S, Zhuang Y, Qiao S, Wang F, Wang S, Lv Z. DCTGM: A Novel Dual-channel Transformer Graph Model for miRNA-disease Association Prediction. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10092-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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10
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SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder. Cells 2022; 11:cells11243984. [PMID: 36552748 PMCID: PMC9776508 DOI: 10.3390/cells11243984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 11/30/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.
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11
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Huang B, Huang H, Zhang S, Zhang D, Shi Q, Liu J, Guo J. Artificial intelligence in pancreatic cancer. Am J Cancer Res 2022; 12:6931-6954. [PMID: 36276650 PMCID: PMC9576619 DOI: 10.7150/thno.77949] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/24/2022] [Indexed: 11/30/2022] Open
Abstract
Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.
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Affiliation(s)
- Bowen Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Haoran Huang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Shuting Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Dingyue Zhang
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.,School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Qingya Shi
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Jianzhou Liu
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Junchao Guo
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
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12
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MHDMF: Prediction of miRNA-disease associations based on Deep Matrix Factorization with Multi-source Graph Convolutional Network. Comput Biol Med 2022; 149:106069. [PMID: 36115300 DOI: 10.1016/j.compbiomed.2022.106069] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 07/31/2022] [Accepted: 08/27/2022] [Indexed: 11/24/2022]
Abstract
A growing number of works have proved that microRNAs (miRNAs) are a crucial biomarker in diverse bioprocesses affecting various diseases. As a good complement to high-cost wet experiment-based methods, numerous computational prediction methods have sprung up. However, there are still challenges that exist in making effective use of high false-negative associations and multi-source information for finding the potential associations. In this work, we develop an end-to-end computational framework, called MHDMF, which integrates the multi-source information on a heterogeneous network to discover latent disease-miRNA associations. Since high false-negative exist in the miRNA-disease associations, MHDMF utilizes the multi-source Graph Convolutional Network (GCN) to correct the false-negative association by reformulating the miRNA-disease association score matrix. The score matrix reformulation is based on different similarity profiles and known associations between miRNAs, genes, and diseases. Then, MHDMF employs Deep Matrix Factorization (DMF) to predict the miRNA-disease associations based on reformulated miRNA-disease association score matrix. The experimental results show that the proposed framework outperforms highly related comparison methods by a large margin on tasks of miRNA-disease association prediction. Furthermore, case studies suggest that MHDMF could be a convenient and efficient tool and may supply a new way to think about miRNA-disease association prediction.
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13
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Xu L, Li X, Yang Q, Tan L, Liu Q, Liu Y. Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases. Front Genet 2022; 13:936823. [PMID: 35903359 PMCID: PMC9314862 DOI: 10.3389/fgene.2022.936823] [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: 05/05/2022] [Accepted: 06/08/2022] [Indexed: 11/18/2022] Open
Abstract
Substantial evidence has shown that microRNAs are crucial for biological processes within complex human diseases. Identifying the association of miRNA–disease pairs will contribute to accelerating the discovery of potential biomarkers and pathogenesis. Researchers began to focus on constructing computational models to facilitate the progress of disease pathology and clinical medicine by identifying the potential disease-related miRNAs. However, most existing computational methods are expensive, and their use is limited to unobserved relationships for unknown miRNAs (diseases) without association information. In this manuscript, we proposed a creatively semi-supervised model named bidirectional generative adversarial network for miRNA-disease association prediction (BGANMDA). First, we constructed a microRNA similarity network, a disease similarity network, and Gaussian interaction profile kernel similarity based on the known miRNA–disease association and comprehensive similarity of miRNAs (diseases). Next, an integrated similarity feature network with the full underlying relationships of miRNA–disease pairwise was obtained. Then, the similarity feature network was fed into the BGANMDA model to learn advanced traits in latent space. Finally, we ranked an association score list and predicted the associations between miRNA and disease. In our experiment, a five-fold cross validation was applied to estimate BGANMDA’s performance, and an area under the curve (AUC) of 0.9319 and a standard deviation of 0.00021 were obtained. At the same time, in the global and local leave-one-out cross validation (LOOCV), the AUC value and standard deviation of BGANMDA were 0.9116 ± 0.0025 and 0.8928 ± 0.0022, respectively. Furthermore, BGANMDA was employed in three different case studies to validate its prediction capability and accuracy. The experimental results of the case studies showed that 46, 46, and 48 of the top 50 prediction lists had been identified in previous studies.
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Affiliation(s)
- Long Xu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Xiaokun Li
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Heilongjiang University, Harbin, China
- *Correspondence: Xiaokun Li, ; Yong Liu,
| | - Qiang Yang
- School of Electronic Engineering, Heilongjiang University, Harbin, China
| | - Long Tan
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Qingyuan Liu
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Heilongjiang University, Harbin, China
| | - Yong Liu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
- *Correspondence: Xiaokun Li, ; Yong Liu,
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14
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Identification of MiRNA–Disease Associations Based on Information of Multi-Module and Meta-Path. Molecules 2022; 27:molecules27144443. [PMID: 35889314 PMCID: PMC9321348 DOI: 10.3390/molecules27144443] [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: 05/17/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 12/10/2022] Open
Abstract
Cumulative research reveals that microRNAs (miRNAs) are involved in many critical biological processes including cell proliferation, differentiation and apoptosis. It is of great significance to figure out the associations between miRNAs and human diseases that are the basis for finding biomarkers for diagnosis and targets for treatment. To overcome the time-consuming and labor-intensive problems faced by traditional experiments, a computational method was developed to identify potential associations between miRNAs and diseases based on the graph attention network (GAT) with different meta-path mode and support vector (SVM). Firstly, we constructed a multi-module heterogeneous network based on the meta-path and learned the latent features of different modules by GAT. Secondly, we found the average of the latent features with weight to obtain a final node representation. Finally, we characterized miRNA–disease-association pairs with the node representation and trained an SVM to recognize potential associations. Based on the five-fold cross-validation and benchmark datasets, the proposed method achieved an area under the precision–recall curve (AUPR) of 0.9379 and an area under the receiver–operating characteristic curve (AUC) of 0.9472. The results demonstrate that our method has an outstanding practical application performance and can provide a reference for the discovery of new biomarkers and therapeutic targets.
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Ji C, Wang Y, Gao Z, Li L, Ni J, Zheng C. A Semi-Supervised Learning Method for MiRNA-Disease Association Prediction Based on Variational Autoencoder. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2049-2059. [PMID: 33735084 DOI: 10.1109/tcbb.2021.3067338] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
MicroRNAs (miRNAs) are a class of non-coding RNAs that play critical role in many biological processes, such as cell growth, development, differentiation and aging. Increasing studies have revealed that miRNAs are closely involved in many human diseases. Therefore, the prediction of miRNA-disease associations is of great significance to the study of the pathogenesis, diagnosis and intervention of human disease. However, biological experimentally methods are usually expensive in time and money, while computational methods can provide an efficient way to infer the underlying disease-related miRNAs. In this study, we propose a novel method to predict potential miRNA-disease associations, called SVAEMDA. Our method mainly consider the miRNA-disease association prediction as semi-supervised learning problem. SVAEMDA integrates disease semantic similarity, miRNA functional similarity and respective Gaussian interaction profile (GIP) similarities. The integrated similarities are used to learn the representations of diseases and miRNAs. SVAEMDA trains a variational autoencoder based predictor by using known miRNA-disease associations, with the form of concatenated dense vectors. Reconstruction probability of the predictor is used to measure the correlation of the miRNA-disease pairs. Experimental results show that SVAEMDA outperforms other stat-of-the-art methods. AUC values of SVAEMDA of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV) are 0.9464 and 0.9428 respectively. In addition, case studies of three common human diseases indicate that SVAEMDA obtains 100 percent of the top 50 predicted candidates in the benchmark databases. Therefore, SVAEMDA can efficiently and accurately predict the potential associations between diseases and miRNAs.
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16
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Yu S, Wang H, Liu T, Liang C, Luo J. A knowledge-driven network for fine-grained relationship detection between miRNA and disease. Brief Bioinform 2022; 23:6551111. [PMID: 35323892 DOI: 10.1093/bib/bbac058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/30/2022] [Accepted: 02/04/2022] [Indexed: 12/12/2022] Open
Abstract
Increasing biological evidence indicated that microRNAs (miRNAs) play a vital role in exploring the pathogenesis of various human diseases (especially in tumors). Mining disease-related miRNAs is of great significance for the clinical diagnosis and treatment of diseases. Compared with the traditional experimental methods with the significant limitations of high cost, long cycle and small scale, the methods based on computing have the advantages of being cost-effective. However, although the current methods based on computational biology can accurately predict the correlation between miRNAs and disease, they can not predict the detailed association information at a fine level. We propose a knowledge-driven approach to the fine-grained prediction of disease-related miRNAs (KDFGMDA). Different from the previous methods, this method can finely predict the clear associations between miRNA and disease, such as upregulation, downregulation or dysregulation. Specifically, KDFGMDA extracts triple information from massive experimental data and existing datasets to construct a knowledge graph and then trains a depth graph representation learning model based on knowledge graph to complete fine-grained prediction tasks. Experimental results show that KDFGMDA can predict the relationship between miRNA and disease accurately, which is of far-reaching significance for medical clinical research and early diagnosis, prevention and treatment of diseases. Additionally, the results of case studies on three types of cancers, Kaplan-Meier survival analysis and expression difference analysis further provide the effectiveness and feasibility of KDFGMDA to detect potential candidate miRNAs. Availability: Our work can be downloaded from https://github.com/ShengPengYu/KDFGMDA.
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Affiliation(s)
- Shengpeng Yu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Hong Wang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Tianyu Liu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China
| | - Jiawei Luo
- School of Information Science and Engineering, Hunan University, Changsha, 410082, China
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17
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Li J, Liu T, Wang J, Li Q, Ning C, Yang Y. MvKFN-MDA: Multi-view Kernel Fusion Network for miRNA-disease association prediction. Artif Intell Med 2021; 118:102115. [PMID: 34412838 DOI: 10.1016/j.artmed.2021.102115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 05/13/2021] [Accepted: 05/21/2021] [Indexed: 12/01/2022]
Abstract
Predicting the associations between microRNAs (miRNAs) and diseases is of great significance for identifying miRNAs related to human diseases. Since it is time-consuming and costly to identify the association between miRNA and disease through biological experiments, computational methods are currently used as an effective supplement to identify the potential association between disease and miRNA. This paper presents a Multi-view Kernel Fusion Network (MvKFN) based prediction method (MvKFN-MDA) to address the problem of miRNA-disease associations prediction. A novel multiple kernel fusion framework Multi-view Kernel Fusion Network (MvKFN) is first proposed to effectively fuse different views similarity kernels constructed from different data sources in a highly nonlinear way. Using MvKFNs, both different base similarity kernels for miRNA, such as sequence, functional, semantic, Gaussian profile kernels and different base similarity kernels for diseases, such as semantic, Gaussian profile kernel are nonlinearly fused into two integrated similarity kernels, one for miRNA, another for disease. Then, miRNA and disease feature representations are extracted from the miRNA and disease integrated similarity kernels respectively. These features are then fed into a neural matrix completion framework which finally outputs the association prediction scores. The parameters of MvKFN-MDA are learned based on the known miRNA-disease association matrix in a supervised end-to-end way. We compare the proposed method with other state-of-the-art methods. The AUCs of our proposed method were superior to the existing methods in both 5-FCV and LOOCV on two open experimental datasets. Furthermore, 49, 48, and 47 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma, and kidney cancer, are verified respectively using experimental literature. Finally, 100% accuracy from the top 50 predicted miRNAs is achieved when breast cancer is used as a case study to evaluate the ability of MvKFN-MDA for predicting a new disease without any known related miRNAs.
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Affiliation(s)
- Jin Li
- School of Software, Yunnan University, Kunming, China; Kunming Key Laboratory of Data Science and Intelligent Computing, Kunming, China
| | - Tao Liu
- School of Software, Yunnan University, Kunming, China
| | - Jingru Wang
- School of Software, Yunnan University, Kunming, China
| | - Qing Li
- First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chenxi Ning
- School of Software, Yunnan University, Kunming, China
| | - Yun Yang
- School of Software, Yunnan University, Kunming, China; Kunming Key Laboratory of Data Science and Intelligent Computing, Kunming, China.
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18
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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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19
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Ji BY, You ZH, Wang Y, Li ZW, Wong L. DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding. iScience 2021; 24:102455. [PMID: 34041455 PMCID: PMC8141887 DOI: 10.1016/j.isci.2021.102455] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 03/02/2021] [Accepted: 04/19/2021] [Indexed: 12/24/2022] Open
Abstract
Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments. In this study, we propose a computational machine learning-based method (DANE-MDA) that preserves integrated structure and attribute features via deep attributed network embedding to predict potential miRNA-disease associations. Specifically, the integrated features are extracted by using deep stacked auto-encoder on the diverse orders of matrixes containing structure and attribute information and are then trained by using random forest classifier. Under 5-fold cross-validation experiments, DANE-MDA yielded average accuracy, sensitivity, and AUC at 85.59%, 84.23%, and 0.9264 in term of HMDD v3.0 dataset, and 83.21%, 80.39%, and 0.9113 in term of HMDD v2.0 dataset, respectively. Additionally, case studies on breast, colon, and lung neoplasms related disease show that 47, 47, and 46 of the top 50 miRNAs can be predicted and retrieved in the other database. A computational machine learning-based method for miRNA-disease association prediction Preserve structure and attribute features via deep attributed network embedding Capture the interaction between two kinds of features from diverse degrees of proximity Extract the higher-order features via deep stacked auto-encoder neural network
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Affiliation(s)
- Bo-Ya Ji
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of the Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of the Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Yi Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Leon Wong
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China.,University of the Chinese Academy of Sciences, Beijing 100049, China.,Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China
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20
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Li A, Deng Y, Tan Y, Chen M. A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method. PLoS One 2021; 16:e0252971. [PMID: 34138933 PMCID: PMC8211179 DOI: 10.1371/journal.pone.0252971] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/26/2021] [Indexed: 12/27/2022] Open
Abstract
A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA-disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA-disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease-miRNA association prediction model.
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Affiliation(s)
- Ang Li
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
| | - Yingwei Deng
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
- Hainan Key Laboratory for Computational Science and Application, Haikou, China
| | - Yan Tan
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
| | - Min Chen
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
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21
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Tang X, Luo J, Shen C, Lai Z. Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction. Brief Bioinform 2021; 22:6271996. [PMID: 33963829 DOI: 10.1093/bib/bbab174] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/08/2021] [Accepted: 04/09/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of the discovery and treatment of disease. Since uncovering associations via traditional experimental methods is complicated and time-consuming, many computational methods have been proposed to identify the potential associations. However, there are still challenges in accurately determining potential associations between miRNA and disease by using multisource data. RESULTS In this study, we develop a Multi-view Multichannel Attention Graph Convolutional Network (MMGCN) to predict potential miRNA-disease associations. Different from simple multisource information integration, MMGCN employs GCN encoder to obtain the features of miRNA and disease in different similarity views, respectively. Moreover, our MMGCN can enhance the learned latent representations for association prediction by utilizing multichannel attention, which adaptively learns the importance of different features. Empirical results on two datasets demonstrate that MMGCN model can achieve superior performance compared with nine state-of-the-art methods on most of the metrics. Furthermore, we prove the effectiveness of multichannel attention mechanism and the validity of multisource data in miRNA and disease association prediction. Case studies also indicate the ability of the method for discovering new associations.
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Affiliation(s)
- Xinru Tang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Jiawei Luo
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Cong Shen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
| | - Zihan Lai
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410083, China
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22
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Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Brief Bioinform 2021; 22:6261915. [PMID: 34009265 DOI: 10.1093/bib/bbab165] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Xuhong Wang
- School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, China
| | - Qiuying Dai
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, China
| | | | | | - Dennis Russell Salahub
- Department of Chemistry, University of Calgary, Fellow Royal Society of Canada and Fellow of the American Association for the Advancement of Science, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
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23
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Ji C, Gao Z, Ma X, Wu Q, Ni J, Zheng C. AEMDA: inferring miRNA-disease associations based on deep autoencoder. Bioinformatics 2021; 37:66-72. [PMID: 32726399 DOI: 10.1093/bioinformatics/btaa670] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 05/27/2020] [Accepted: 07/20/2020] [Indexed: 12/19/2022] Open
Abstract
MOTIVATION MicroRNAs (miRNAs) are a class of non-coding RNAs that play critical roles in various biological processes. Many studies have shown that miRNAs are closely related to the occurrence, development and diagnosis of human diseases. Traditional biological experiments are costly and time consuming. As a result, effective computational models have become increasingly popular for predicting associations between miRNAs and diseases, which could effectively boost human disease diagnosis and prevention. RESULTS We propose a novel computational framework, called AEMDA, to identify associations between miRNAs and diseases. AEMDA applies a learning-based method to extract dense and high-dimensional representations of diseases and miRNAs from integrated disease semantic similarity, miRNA functional similarity and heterogeneous related interaction data. In addition, AEMDA adopts a deep autoencoder that does not need negative samples to retrieve the underlying associations between miRNAs and diseases. Furthermore, the reconstruction error is used as a measurement to predict disease-associated miRNAs. Our experimental results indicate that AEMDA can effectively predict disease-related miRNAs and outperforms state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/CunmeiJi/AEMDA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cunmei Ji
- School of Software, Qufu Normal University, Qufu 273165, China
| | - Zhen Gao
- School of Software, Qufu Normal University, Qufu 273165, China
| | - Xu Ma
- School of Software, Qufu Normal University, Qufu 273165, China
| | - Qingwen Wu
- School of Software, Qufu Normal University, Qufu 273165, China
| | - Jiancheng Ni
- School of Software, Qufu Normal University, Qufu 273165, China
| | - Chunhou Zheng
- School of Software, Qufu Normal University, Qufu 273165, China.,School of Computer Science and Technology, Anhui University, Hefei 230601, China
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Liu D, Huang Y, Nie W, Zhang J, Deng L. SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost. BMC Bioinformatics 2021; 22:219. [PMID: 33910505 PMCID: PMC8082881 DOI: 10.1186/s12859-021-04135-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/14/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. RESULTS In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. CONCLUSION The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.
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Affiliation(s)
- Dayun Liu
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Yibiao Huang
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Wenjuan Nie
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Jiaxuan Zhang
- Department of Cognitive Science, University of California San Diego, La Jolla, 92093, USA
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China.
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25
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Prediction of miRNA-Disease Association Using Deep Collaborative Filtering. BIOMED RESEARCH INTERNATIONAL 2021; 2021:6652948. [PMID: 33681362 PMCID: PMC7929672 DOI: 10.1155/2021/6652948] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 02/01/2021] [Accepted: 02/10/2021] [Indexed: 12/12/2022]
Abstract
The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma.
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26
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Ding Y, Tian LP, Lei X, Liao B, Wu FX. Variational graph auto-encoders for miRNA-disease association prediction. Methods 2020; 192:25-34. [PMID: 32798654 DOI: 10.1016/j.ymeth.2020.08.004] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/03/2020] [Accepted: 08/08/2020] [Indexed: 02/07/2023] Open
Abstract
Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing. In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity, and known miRNA-disease associations. Then, VGAE-MDA constructs two sub-networks: miRNA-based network and disease-based network. Combining the representations based on the heterogeneous network, two variational graph auto-encoders (VGAE) are deployed for calculating the miRNA-disease association scores from two sub-networks, respectively. Lastly, VGAE-MDA obtains the final predicted association score for a miRNA-disease pair by integrating the scores from these two trained networks. Unlike the previous model, the VGAE-MDA can mitigate the effect of noises from random selection of negative samples. Besides, the use of graph convolutional neural (GCN) network can naturally incorporate the node features from the graph structure while the variational autoencoder (VAE) makes use of latent variables to predict associations from the perspective of data distribution. The experimental results show that VGAE-MDA outperforms the state-of-the-art approaches in miRNA-disease association prediction. Besides, the effectiveness of our model has been further demonstrated by case studies.
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Affiliation(s)
- Yulian Ding
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Li-Ping Tian
- School of Information, Beijing Wuzi University, Beijing 101125, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada; Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada; Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada.
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27
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Lin HM, Xue XF, Wang XG, Dang SC, Gu M. Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. Artif Intell Gastroenterol 2020; 1:19-29. [DOI: 10.35712/aig.v1.i1.19] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/12/2020] [Accepted: 07/16/2020] [Indexed: 02/06/2023] Open
Abstract
Pancreatic cancer is a complex cancer of the digestive tract. Diagnosis and treatment can be very difficult because of unclear early symptoms, the deep anatomical location of cancer tissues, and the high degree of cancer cell invasion. The prognosis is extremely poor; the 5-year survival rate of patients with pancreatic cancer is less than 1%. Artificial intelligence (AI) has great potential for application in the medical field. In addition to AI-based applications, such as disease data processing, imaging, and pathological image recognition, robotic surgery has revolutionized surgical procedures. To better understand the current role of AI in pancreatic cancer and predict future development trends, this article comprehensively reports the application of AI to the diagnosis, treatment, and prognosis of pancreatic cancer.
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Affiliation(s)
- Hai-Min Lin
- Department of General Surgery, the Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
| | - Xiao-Fei Xue
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Xiao-Gang Wang
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Sheng-Chun Dang
- Department of General Surgery, the Affiliated Hospital, Jiangsu University, Zhenjiang 212001, Jiangsu Province, China
- Department of General Surgery, Pucheng Hospital, Weinan 715500, Shaanxi Province, China
| | - Min Gu
- Department of Oncology, Zhenjiang Hospital of Traditional Chinese and Western Medicine, Zhenjiang 212000 Jiangsu Province, China
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28
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Application of artificial intelligence for the diagnosis, treatment, and prognosis of pancreatic cancer. Artif Intell Gastroenterol 2020. [DOI: 10.35712/wjg.v1.i1.19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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29
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Zhang Y, Chen M, Cheng X, Wei H. MSFSP: A Novel miRNA-Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection. Front Genet 2020; 11:389. [PMID: 32425980 PMCID: PMC7204399 DOI: 10.3389/fgene.2020.00389] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 03/27/2020] [Indexed: 12/11/2022] Open
Abstract
Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.
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Affiliation(s)
- Yi Zhang
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Min Chen
- School of Computer Science and Technology, Hunan Institute of Technology, Hengyang, China
| | - Xiaohui Cheng
- School of Information Science and Engineering, Guilin University of Technology, Guilin, China
| | - Hanyan Wei
- School of Pharmacy, Guilin Medical University, Guilin, China
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30
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Wu Q, Wang Y, Gao Z, Ni J, Zheng C. MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association. Front Genet 2020; 11:354. [PMID: 32351545 PMCID: PMC7174776 DOI: 10.3389/fgene.2020.00354] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 03/23/2020] [Indexed: 12/17/2022] Open
Abstract
Accumulating biological and clinical evidence has confirmed the important associations between microRNAs (miRNAs) and a variety of human diseases. Predicting disease-related miRNAs is beneficial for understanding the molecular mechanisms of pathological conditions at the miRNA level, and facilitating the finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. However, the challenge for researchers is to establish methods that can effectively combine different datasets and make reliable predictions. In this work, we propose the method of Multi-Similarity based Combinative Hypergraph Learning for Predicting MiRNA-disease Association (MSCHLMDA). To establish this method, complex features were extracted by two measures for each miRNA-disease pair. Then, K-nearest neighbor (KNN) and K-means algorithm were used to construct two different hypergraphs. Finally, results from combinative hypergraph learning were used for predicting miRNA-disease association. In order to evaluate the prediction performance of our method, leave-one-out cross validation and 5-fold cross validation was implemented, showing that our method had significantly improved prediction performance compared to previously used methods. Moreover, three case studies on different human complex diseases were performed, which further demonstrated the predictive performance of MSCHLMDA. It is anticipated that MSCHLMDA would become an excellent complement to the biomedical research field in the future.
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Affiliation(s)
- Qingwen Wu
- School of Software, Qufu Normal University, Qufu, China
| | - Yutian Wang
- School of Software, Qufu Normal University, Qufu, China
| | - Zhen Gao
- School of Software, Qufu Normal University, Qufu, China
| | - Jiancheng Ni
- School of Software, Qufu Normal University, Qufu, China
| | - Chunhou Zheng
- School of Software, Qufu Normal University, Qufu, China.,School of Computer Science and Technology, Anhui University, Hefei, China
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31
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Li J, Zhang S, Liu T, Ning C, Zhang Z, Zhou W. Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction. Bioinformatics 2020; 36:2538-2546. [DOI: 10.1093/bioinformatics/btz965] [Citation(s) in RCA: 100] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 12/17/2019] [Accepted: 12/31/2019] [Indexed: 12/26/2022] Open
Abstract
AbstractMotivationPredicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA.ResultsWe present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs.Availability and implementationhttps://github.com/ljatynu/NIMCGCN/Supplementary informationSupplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jin Li
- School of Software, Yunnan University, Kunming 650091, China
| | - Sai Zhang
- School of Software, Yunnan University, Kunming 650091, China
| | - Tao Liu
- School of Software, Yunnan University, Kunming 650091, China
| | - Chenxi Ning
- School of Software, Yunnan University, Kunming 650091, China
| | - Zhuoxuan Zhang
- School of Software, Yunnan University, Kunming 650091, China
| | - Wei Zhou
- School of Software, Yunnan University, Kunming 650091, China
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32
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Predicting miRNA-Disease Associations by Incorporating Projections in Low-Dimensional Space and Local Topological Information. Genes (Basel) 2019; 10:genes10090685. [PMID: 31500152 PMCID: PMC6770973 DOI: 10.3390/genes10090685] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2019] [Revised: 08/31/2019] [Accepted: 09/03/2019] [Indexed: 12/14/2022] Open
Abstract
Predicting the potential microRNA (miRNA) candidates associated with a disease helps in exploring the mechanisms of disease development. Most recent approaches have utilized heterogeneous information about miRNAs and diseases, including miRNA similarities, disease similarities, and miRNA-disease associations. However, these methods do not utilize the projections of miRNAs and diseases in a low-dimensional space. Thus, it is necessary to develop a method that can utilize the effective information in the low-dimensional space to predict potential disease-related miRNA candidates. We proposed a method based on non-negative matrix factorization, named DMAPred, to predict potential miRNA-disease associations. DMAPred exploits the similarities and associations of diseases and miRNAs, and it integrates local topological information of the miRNA network. The likelihood that a miRNA is associated with a disease also depends on their projections in low-dimensional space. Therefore, we project miRNAs and diseases into low-dimensional feature space to yield their low-dimensional and dense feature representations. Moreover, the sparse characteristic of miRNA-disease associations was introduced to make our predictive model more credible. DMAPred achieved superior performance for 15 well-characterized diseases with AUCs (area under the receiver operating characteristic curve) ranging from 0.860 to 0.973 and AUPRs (area under the precision-recall curve) ranging from 0.118 to 0.761. In addition, case studies on breast, prostatic, and lung neoplasms demonstrated the ability of DMAPred to discover potential disease-related miRNAs.
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