51
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Ji BY, You ZH, Chen ZH, Wong L, Yi HC. NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. BMC Bioinformatics 2020; 21:401. [PMID: 32912137 PMCID: PMC7646193 DOI: 10.1186/s12859-020-03716-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 08/19/2020] [Indexed: 12/25/2022] Open
Abstract
Background As an important non-coding RNA, microRNA (miRNA) plays a significant role in a series of life processes and is closely associated with a variety of Human diseases. Hence, identification of potential miRNA-disease associations can make great contributions to the research and treatment of Human diseases. However, to our knowledge, many existing computational methods only utilize the single type of known association information between miRNAs and diseases to predict their potential associations, without focusing on their interactions or associations with other types of molecules. Results In this paper, we propose a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information. Firstly, a heterogeneous network is constructed by integrating known associations among miRNA, protein and disease, and the network representation method Learning Graph Representations with Global Structural Information (GraRep) is implemented to learn the behavior information of miRNAs and diseases in the network. Then, the behavior information of miRNAs and diseases is combined with the attribute information of them to represent miRNA-disease association pairs. Finally, the prediction model is established based on the Random Forest algorithm. Under the five-fold cross validation, the proposed NEMPD model obtained average 85.41% prediction accuracy with 80.96% sensitivity at the AUC of 91.58%. Furthermore, the performance of NEMPD is also validated by the case studies. Among the top 50 predicted disease-related miRNAs, 48 (breast neoplasms), 47 (colon neoplasms), 47 (lung neoplasms) were confirmed by two other databases. Conclusions The proposed NEMPD model has a good performance in predicting the potential associations between miRNAs and diseases, and has great potency in the field of miRNA-disease association prediction in the future.
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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 Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Zhan-Heng Chen
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Leon Wong
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Hai-Cheng Yi
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
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52
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Zheng K, You ZH, Wang L, Guo ZH. iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm. Comput Struct Biotechnol J 2020; 18:2391-2400. [PMID: 33005302 PMCID: PMC7508695 DOI: 10.1016/j.csbj.2020.08.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 11/30/2022] Open
Abstract
Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively.
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Affiliation(s)
- Kai Zheng
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
| | - Lei Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
- College of Information Science and Engineering, Zaozhuang University, Zaozhuang 277100, China
| | - Zhen-Hao Guo
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Science, Urumqi 830011, China
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53
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Chen X, Li TH, Zhao Y, Wang CC, Zhu CC. Deep-belief network for predicting potential miRNA-disease associations. Brief Bioinform 2020; 22:5898648. [PMID: 34020550 DOI: 10.1093/bib/bbaa186] [Citation(s) in RCA: 112] [Impact Index Per Article: 22.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/09/2020] [Accepted: 07/21/2020] [Indexed: 12/14/2022] Open
Abstract
MicroRNA (miRNA) plays an important role in the occurrence, development, diagnosis and treatment of diseases. More and more researchers begin to pay attention to the relationship between miRNA and disease. Compared with traditional biological experiments, computational method of integrating heterogeneous biological data to predict potential associations can effectively save time and cost. Considering the limitations of the previous computational models, we developed the model of deep-belief network for miRNA-disease association prediction (DBNMDA). We constructed feature vectors to pre-train restricted Boltzmann machines for all miRNA-disease pairs and applied positive samples and the same number of selected negative samples to fine-tune DBN to obtain the final predicted scores. Compared with the previous supervised models that only use pairs with known label for training, DBNMDA innovatively utilizes the information of all miRNA-disease pairs during the pre-training process. This step could reduce the impact of too few known associations on prediction accuracy to some extent. DBNMDA achieves the AUC of 0.9104 based on global leave-one-out cross validation (LOOCV), the AUC of 0.8232 based on local LOOCV and the average AUC of 0.9048 ± 0.0026 based on 5-fold cross validation. These AUCs are better than other previous models. In addition, three different types of case studies for three diseases were implemented to demonstrate the accuracy of DBNMDA. As a result, 84% (breast neoplasms), 100% (lung neoplasms) and 88% (esophageal neoplasms) of the top 50 predicted miRNAs were verified by recent literature. Therefore, we could conclude that DBNMDA is an effective method to predict potential miRNA-disease associations.
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Affiliation(s)
- Xing Chen
- Artificial Intelligence Research Institute, China University of Mining and Technology
| | - Tian-Hao Li
- School of Information and Control Engineering, China University of Mining and Technology
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology
| | - Chi-Chi Zhu
- School of Information and Control Engineering, China University of Mining and Technology
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54
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Zhu R, Ji C, Wang Y, Cai Y, Wu H. Heterogeneous Graph Convolutional Networks and Matrix Completion for miRNA-Disease Association Prediction. Front Bioeng Biotechnol 2020; 8:901. [PMID: 32974293 PMCID: PMC7468400 DOI: 10.3389/fbioe.2020.00901] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 05/13/2020] [Indexed: 01/21/2023] Open
Abstract
Due to the cost and complexity of biological experiments, many computational methods have been proposed to predict potential miRNA-disease associations by utilizing known miRNA-disease associations and other related information. However, there are some challenges for these computational methods. First, the relationships between miRNAs and diseases are complex. The computational network should consider the local and global influence of neighborhoods from the network. Furthermore, predicting disease-related miRNAs without any known associations is also very important. This study presents a new computational method that constructs a heterogeneous network composed of a miRNA similarity network, disease similarity network, and known miRNA-disease association network. The miRNA similarity considers the miRNAs and their possible families and clusters. The information of each node in heterogeneous network is obtained by aggregating neighborhood information with graph convolutional networks (GCNs), which can pass the information of a node to its intermediate and distant neighbors. Disease-related miRNAs with no known associations can be predicted with the reconstructed heterogeneous matrix. We apply 5-fold cross-validation, leave-one-disease-out cross-validation, and global and local leave-one-out cross-validation to evaluate our method. The corresponding areas under the curves (AUCs) are 0.9616, 0.9946, 0.9656, and 0.9532, confirming that our approach significantly outperforms the state-of-the-art methods. Case studies show that this approach can effectively predict new diseases without any known miRNAs.
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Affiliation(s)
- Rongxiang Zhu
- Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Chaojie Ji
- Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yingying Wang
- Department of Neurology and Stroke Center, The First Affiliated Hospital of Jinan University, Guangzhou, China.,Clinical Neuroscience Institute, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yunpeng Cai
- Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Hongyan Wu
- Joint Engineering Research Center for Health Big Data Intelligent Analysis Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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55
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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: 53] [Impact Index Per Article: 10.6] [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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Zhang D, Liu X, Zhang Q, Chen X. miR-138-5p inhibits the malignant progression of prostate cancer by targeting FOXC1. Cancer Cell Int 2020; 20:297. [PMID: 32669973 PMCID: PMC7350594 DOI: 10.1186/s12935-020-01386-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 06/26/2020] [Indexed: 01/22/2023] Open
Abstract
Background This study aimed to uncover the effect of miR-138-5p on the proliferation and metastasis of PCa cell lines, and further explore the potential regulatory mechanisms via regulating FOXC1. Methods 60 pairs cancer tissues and corresponding paracancerous ones from PCa patients were collected to assess the expression level of miR-138-5p by qRT-PCR. Subsequently, over-expression of miR-138-5p were established to explore the proliferation and metastasis of miR-138-5p in PCa cell lines was analyzed by CCK-8, Transwell assay and Wounding healing assay, respectively. Bioinformatics analysis and luciferase reporter gene assay were performed to search for the target genes of miR-138-5p, and FOXC1 was selected. Finally, the biological role of miR-138-5p and FOXC1 in the progression of PCa was clarified by a series of rescue experiments. Results The results of qRT-PCR revealed that miR-138-5p was lowly expressed in PCa tissues and cell lines. Besides, the PCa patients with low-miR-138-5p had a high Gleason score, lymph node metastasis and poor prognosis of PCa, compared with these patients with high-miR-138-5p. Over-expression of miR-138-5p inhibited the proliferative, migratory and invasive capacities of PC-3 and DU-145 cells. Bioinformatics analysis and luciferase reporter gene assay suggested that FOXC1 was predicted to be the target gene of miR-138-5p. Moreover, FOXC1 expression level was negatively correlated to that of miR-138-5p in PCa tissues. Importantly, over-expression of FOXC1 could reverse miR-138-5p mimic induced-inhibition of PCa malignant progression. Conclusions Downregulated miR-138-5p was closely associated with high Gleason score, more lymph node metastasis and poor prognosis of PCa patients. In addition, miR-138-5p alleviated the malignant progression of PCa by targeting and downregulating FOXC1.
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Affiliation(s)
- Dapeng Zhang
- Department of Urology Surgery, Chifengshi Hospital, Chifeng, 024000 Neimenggu China
| | - Xiaodong Liu
- Department of Urology Surgery, Chifengshi Hospital, Chifeng, 024000 Neimenggu China
| | - Qingwei Zhang
- Department of Urology Surgery, Chifengshi Hospital, Chifeng, 024000 Neimenggu China
| | - Xin Chen
- Department of Urology Surgery, Chifengshi Hospital, Chifeng, 024000 Neimenggu China
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57
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Zhang W, Yao G, Wang J, Yang M, Wang J, Zhang H, Li W. ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs. RNA Biol 2020; 17:943-955. [PMID: 32122231 PMCID: PMC7549653 DOI: 10.1080/15476286.2020.1737441] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/24/2020] [Accepted: 02/25/2020] [Indexed: 12/31/2022] Open
Abstract
Noncoding RNAs (ncRNAs) play critical roles in many critical biological processes and have become a novel class of potential targets and bio-markers for disease diagnosis, therapy, and prognosis. Annotating and analysing ncRNA-disease association data are essential but challenging. Current computational resources lack comprehensive database platforms to consistently interpret and prioritize ncRNA-disease association data for biomedical investigation and application. Here, we present the ncRPheno database platform (http://lilab2.sysu.edu.cn/ncrpheno), which comprehensively integrates and annotates ncRNA-disease association data and provides novel searches, visualizations, and utilities for association identification and validation. ncRPheno contains 482,751 non-redundant associations between 14,494 ncRNAs and 3,210 disease phenotypes across 11 species with supporting evidence in the literature. A scoring model was refined to prioritize the associations based on evidential metrics. Moreover, ncRPheno provides user-friendly web interfaces, novel visualizations, and programmatic access to enable easy exploration, analysis, and utilization of the association data. A case study through ncRPheno demonstrated a comprehensive landscape of ncRNAs dysregulation associated with 22 cancers and uncovered 821 cancer-associated common ncRNAs. As a unique database platform, ncRPheno outperforms the existing similar databases in terms of data coverage and utilities, and it will assist studies in encoding ncRNAs associated with phenotypes ranging from genetic disorders to complex diseases. ABBREVIATIONS APIs: application programming interfaces; circRNA: circular RNA; ECO: Evidence & Conclusion Ontology; EFO: Experimental Factor Ontology; FDR: false discovery rate; GO: Gene Ontology; GWAS: genome wide association studies; HPO: Human Phenotype Ontology; ICGC: International Cancer Genome Consortium; lncRNA: long noncoding RNA; miRNA: micro RNA; ncRNA: noncoding RNA; NGS: next generation sequencing; OMIM: Online Mendelian Inheritance in Man; piRNA: piwi-interacting RNA; snoRNA: small nucleolar RNA; TCGA: The Cancer Genome Atlas.
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Affiliation(s)
- Wenliang Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Guocai Yao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jianbo Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Minglei Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jing Wang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control, Sun Yat-Sen University, Ministry of Education, China
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58
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Bai N, Ma Y, Zhao J, Li B. Knockdown of lncRNA HCP5 Suppresses the Progression of Colorectal Cancer by miR-299-3p/PFN1/AKT Axis. Cancer Manag Res 2020; 12:4747-4758. [PMID: 32606965 PMCID: PMC7310975 DOI: 10.2147/cmar.s255866] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2020] [Accepted: 06/01/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is one of the most common malignant tumors in the digestive system. The lncRNA HCP5 has been reported to affect the progression of tumor in several types of cancer. Here, in this research, we focus on the role and function of lncRNA HCP5 in human colorectal cancer. MATERIALS AND METHODS Tissue samples from colorectal cancer patients were used for detecting the expression of HCP5 by qRT-PCR. Proliferation, migration, invasion and apoptotic cells were assessed by CCK-8, colony formation, transwell assays and flow cytometry in SW480 and HCT-116 cells. The interactions between miR-299-3p and HCP5 or PFN1 were analyzed and confirmed by online database and luciferase reporter assays. The changes in PFN1 and AKT proteins were measured by Western blot. In vivo experiment was used to confirm the role of HCP5 in CRC. RESULTS The expression of HCP5 had a higher level in colorectal cancer samples and cells by qRT-PCR, comparing with the normal colorectal tissues and human normal colon epithelial cell. It was revealed that knockdown of HCP5 inhibited viabilities, migration and invasion, while inducing apoptosis in SW480 and HCT-116 cells. Then, HCP5 negatively regulated the expressions of miR-299-3p, which negatively regulated the expressions of PFN1 by targeting PFN1. Furthermore, miR-299-3p inhibitor could alleviate the inhibiting effect by si-HCP5 on cell process of SW480 and HCT-116 cells. In addition, the lncHCP5/miR-299-3p/PFN1 axis could affect the progression of CRC through activating the AKT signaling. Last, we confirmed that knockdown of HCP5 inhibited the progression of CRC with an in vivo experiment. CONCLUSION The experiments and analyses support our hypothesis that knockdown of lncRNA HCP5 suppresses the progression of colorectal cancer by miR-299-3p/PFN1/AKT axis.
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Affiliation(s)
- Ni Bai
- Department of Laboratory Medicine, The Affiliated Xi’an Centre Hospital of Xi’an Jiaotong University College of Medicine, Xi’an, Shaanxi710003, People’s Republic of China
| | - Ying Ma
- Department of Laboratory Medicine, The Affiliated Xi’an Centre Hospital of Xi’an Jiaotong University College of Medicine, Xi’an, Shaanxi710003, People’s Republic of China
| | - Jia Zhao
- Department of Laboratory Medicine, The Affiliated Xi’an Centre Hospital of Xi’an Jiaotong University College of Medicine, Xi’an, Shaanxi710003, People’s Republic of China
| | - Bo Li
- Department of Laboratory Medicine, The Affiliated Xi’an Centre Hospital of Xi’an Jiaotong University College of Medicine, Xi’an, Shaanxi710003, People’s Republic of China
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FCGCNMDA: predicting miRNA-disease associations by applying fully connected graph convolutional networks. Mol Genet Genomics 2020; 295:1197-1209. [DOI: 10.1007/s00438-020-01693-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 05/27/2020] [Indexed: 01/02/2023]
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Li X, Zhou S, Fan T, Feng X. lncRNA DGCR 5/miR‑27a‑3p/BNIP3 promotes cell apoptosis in pancreatic cancer by regulating the p38 MAPK pathway. Int J Mol Med 2020; 46:729-739. [PMID: 32626951 PMCID: PMC7307863 DOI: 10.3892/ijmm.2020.4632] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 04/14/2020] [Indexed: 12/13/2022] Open
Abstract
Long non‑coding RNA (lncRNA) DGCR5 has been identified as a tumor suppressor in several types of cancer. However, its biological functions in pancreatic cancer (PaCa) have not yet been fully elucidated. The present study was designed to investigate the role of lncRNA DGCR5 in the regulation of PaCa cell apoptosis. For this purpose, lncRNA DGCR5, miR‑27a‑3p and Bcl‑2/adenovirus E1B‑19kDa‑interacting protein 3 (BNIP3) expression levels were examined by reverse transcription‑quantitative (RT‑qPCR) and western blot analysis, respectively. RNA pull‑down assay was used to verify DGCR5 as a target of miR‑27a‑3p and dual luciferase reporter assay was used to clarify whether miR‑27a‑3p targets the BNIP3 3' UTR. In addition, PaCa cell apoptosis was assessed by flow cytometry. Recombinant plasmids and cell transfection were performed to modulate the endogenous expression of related genes. Thereafter, the role of DGCR5 in PaCa was analyzed using a nude mouse model of PaCa. lncRNA DGCR5 was found to be downregulated in PaCa tissues and cells. DGCR5 functioned as a decoy of miR‑27a‑3p, and BNIP3 was negatively regulated by miR‑27a‑3p. Following the transfection of DGCR5 plasmid into PaCa cells, the expression of miR‑27a‑3p was downregulated, and this downregulation was reversed following transfection with miR‑27a‑3p mimic. In addition, DGCR5 regulated the BNIP3 and p38 MAPK pathways via miR‑27a‑3p and promoted PaCa cell apoptosis via the miR‑27a‑3p/BNIP3 pathway. The results of in vivo experiments also indicated the positive effects of DGCR5 on a nude mouse model of PaCa. On the whole, the findings of the present study indicate that lncRNA DGCR5 upregulates the BNIP3 and p38 MAPK pathways via miR‑27a‑3p to promote PaCa cell apoptosis, thereby attenuating PaCa development.
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Affiliation(s)
- Xianjie Li
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang 315020, P.R. China
| | - Shanxue Zhou
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang 315020, P.R. China
| | - Tianyi Fan
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang 315020, P.R. China
| | - Xuefeng Feng
- Department of Hepatobiliary Surgery, The Affiliated Hospital of Medical School, Ningbo University, Ningbo, Zhejiang 315020, P.R. China
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Piran M, Karbalaei R, Piran M, Aldahdooh J, Mirzaie M, Ansari-Pour N, Tang J, Jafari M. Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity? Biomolecules 2020; 10:biom10060850. [PMID: 32503292 PMCID: PMC7355924 DOI: 10.3390/biom10060850] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 05/30/2020] [Accepted: 05/31/2020] [Indexed: 12/17/2022] Open
Abstract
Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.
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Affiliation(s)
- Mehran Piran
- Bioinformatics and Computational Biology Research Center, Shiraz University of Medical Sciences, Shiraz P.O. Box 71336-54361, Iran;
| | - Reza Karbalaei
- Department of Biology, Temple University, Philadelphia, PA 19122, USA;
| | - Mehrdad Piran
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran P.O. Box 14177-55469, Iran;
| | - Jehad Aldahdooh
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00270 Helsinki, Finland;
| | - Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran P.O. Box 14115-134, Iran;
| | - Naser Ansari-Pour
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LF, UK;
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00270 Helsinki, Finland;
- Correspondence: (J.T.); (M.J.)
| | - Mohieddin Jafari
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00270 Helsinki, Finland;
- Correspondence: (J.T.); (M.J.)
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Zhu X, Wang X, Zhao H, Pei T, Kuang L, Wang L. BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction. Front Genet 2020; 11:384. [PMID: 32425979 PMCID: PMC7212362 DOI: 10.3389/fgene.2020.00384] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 03/27/2020] [Indexed: 01/04/2023] Open
Abstract
Recent studies have indicated that microRNAs (miRNAs) are closely related to sundry human sophisticated diseases. According to the surmise that functionally similar miRNAs are more likely associated with phenotypically similar diseases, researchers have proposed a variety of valid computational models through integrating known miRNA-disease associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity to discover the potential miRNA-disease relationships in biomedical researches. Taking account of the limitations of previous computational models, a new computational model based on biased heat conduction for MiRNA-Disease Association prediction (BHCMDA) was proposed in this paper, which can achieve the AUC of 0.8890 in LOOCV (Leave-One-Out Cross Validation) and the mean AUC of 0.9060, 0.8931 under the framework of twofold cross validation, fivefold cross validation, respectively. In addition, BHCMDA was further implemented to the case studies of three vital human cancers, and simulation results illustrated that there were 88% (Esophageal Neoplasms), 92% (Colonic Neoplasms) and 92% (Lymphoma) out of top 50 predicted miRNAs having been confirmed by experimental literatures, separately, which demonstrated the good performance of BHCMDA as well. Thence, BHCMDA would be a useful calculative resource for potential miRNA-disease association prediction.
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Affiliation(s)
- Xianyou Zhu
- College of Computer Science and Technology, Hengyang Normal University, Hengyang, China
| | - Xuzai Wang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Haochen Zhao
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Tingrui Pei
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Linai Kuang
- College of Computer Science and Technology, Hengyang Normal University, Hengyang, China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China
| | - Lei Wang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.,College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, China
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63
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Ha J, Park C, Park C, Park S. Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization. Cells 2020; 9:cells9040881. [PMID: 32260218 PMCID: PMC7226829 DOI: 10.3390/cells9040881] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 12/30/2019] [Accepted: 04/01/2020] [Indexed: 12/12/2022] Open
Abstract
The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework—improved prediction of miRNA-disease associations (IMDN)—based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.
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Affiliation(s)
- Jihwan Ha
- Department of Computer Science, Yonsei University, Seoul 03722, Korea; (J.H.); (C.P.)
| | - Chihyun Park
- Department of Computer Science, Yonsei University, Seoul 03722, Korea; (J.H.); (C.P.)
| | - Chanyoung Park
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, OH 61801, USA;
| | - Sanghyun Park
- Department of Computer Science, Yonsei University, Seoul 03722, Korea; (J.H.); (C.P.)
- Correspondence: ; Tel.: +82-2-2123-5714
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64
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Gao Z, Wang YT, Wu QW, Ni JC, Zheng CH. Graph regularized L 2,1-nonnegative matrix factorization for miRNA-disease association prediction. BMC Bioinformatics 2020; 21:61. [PMID: 32070280 PMCID: PMC7029547 DOI: 10.1186/s12859-020-3409-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 02/11/2020] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers. RESULTS Here, we present a computational framework based on graph Laplacian regularized L2, 1-nonnegative matrix factorization (GRL2, 1-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL2,1-NMF framework was used to predict links between microRNAs and diseases. CONCLUSIONS The new method (GRL2, 1-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL2, 1-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.
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Affiliation(s)
- Zhen Gao
- School of Software, Qufu Normal University, Qufu, 273165, China
| | - Yu-Tian Wang
- School of Software, Qufu Normal University, Qufu, 273165, China
| | - Qing-Wen Wu
- School of Software, Qufu Normal University, Qufu, 273165, China
| | - Jian-Cheng Ni
- School of Software, Qufu Normal University, Qufu, 273165, China.
| | - Chun-Hou Zheng
- School of Software, Qufu Normal University, Qufu, 273165, China.
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65
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Peng LH, Zhou LQ, Chen X, Piao X. A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression. Front Bioeng Biotechnol 2020; 8:40. [PMID: 32117922 PMCID: PMC7015868 DOI: 10.3389/fbioe.2020.00040] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 01/17/2020] [Indexed: 12/11/2022] Open
Abstract
As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model’s average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.
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Affiliation(s)
- Li-Hong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Li-Qian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Xue Piao
- School of Medical Informatics, Xuzhou Medical University, Xuzhou, China
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66
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Ha J, Park C, Park C, Park S. IMIPMF: Inferring miRNA-disease interactions using probabilistic matrix factorization. J Biomed Inform 2020; 102:103358. [DOI: 10.1016/j.jbi.2019.103358] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 11/11/2019] [Accepted: 12/12/2019] [Indexed: 12/09/2022]
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67
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Peng L, Liu F, Yang J, Liu X, Meng Y, Deng X, Peng C, Tian G, Zhou L. Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms. Front Genet 2020; 10:1346. [PMID: 32082358 PMCID: PMC7005249 DOI: 10.3389/fgene.2019.01346] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/09/2019] [Indexed: 12/31/2022] Open
Abstract
Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Jialiang Yang
- Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China
| | - Xiaojun Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Yajie Meng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiaojun Deng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Cheng Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Geng Tian
- Department of Sciences, Genesis (Beijing) Co. Ltd., Beijing, China
| | - Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
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68
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Chen X, Sun LG, Zhao Y. NCMCMDA: miRNA-disease association prediction through neighborhood constraint matrix completion. Brief Bioinform 2020; 22:485-496. [PMID: 31927572 DOI: 10.1093/bib/bbz159] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 11/01/2019] [Accepted: 11/11/2019] [Indexed: 12/13/2022] Open
Abstract
Emerging evidence shows that microRNAs (miRNAs) play a critical role in diverse fundamental and important biological processes associated with human diseases. Inferring potential disease related miRNAs and employing them as the biomarkers or drug targets could contribute to the prevention, diagnosis and treatment of complex human diseases. In view of that traditional biological experiments cost much time and resources, computational models would serve as complementary means to uncover potential miRNA-disease associations. In this study, we proposed a new computational model named Neighborhood Constraint Matrix Completion for MiRNA-Disease Association prediction (NCMCMDA) to predict potential miRNA-disease associations. The main task of NCMCMDA was to recover the missing miRNA-disease associations based on the known miRNA-disease associations and integrated disease (miRNA) similarity. In this model, we innovatively integrated neighborhood constraint with matrix completion, which provided a novel idea of utilizing similarity information to assist the prediction. After the recovery task was transformed into an optimization problem, we solved it with a fast iterative shrinkage-thresholding algorithm. As a result, the AUCs of NCMCMDA in global and local leave-one-out cross validation were 0.9086 and 0.8453, respectively. In 5-fold cross validation, NCMCMDA achieved an average AUC of 0.8942 and standard deviation of 0.0015, which demonstrated NCMCMDA's superior performance than many previous computational methods. Furthermore, NCMCMDA was applied to three different types of case studies to further evaluate its prediction reliability and accuracy. As a result, 84% (colon neoplasms), 98% (esophageal neoplasms) and 98% (breast neoplasms) of the top 50 predicted miRNAs were verified by recent literature.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology
| | - Lian-Gang Sun
- School of Information and Control Engineering, China University of Mining and Technology
| | - Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology
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69
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Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization. Genomics 2020; 112:809-819. [DOI: 10.1016/j.ygeno.2019.05.021] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 05/09/2019] [Accepted: 05/24/2019] [Indexed: 12/19/2022]
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70
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An improved random forest-based computational model for predicting novel miRNA-disease associations. BMC Bioinformatics 2019; 20:624. [PMID: 31795954 PMCID: PMC6889672 DOI: 10.1186/s12859-019-3290-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/21/2019] [Indexed: 01/29/2023] Open
Abstract
Background A large body of evidence shows that miRNA regulates the expression of its target genes at post-transcriptional level and the dysregulation of miRNA is related to many complex human diseases. Accurately discovering disease-related miRNAs is conductive to the exploring of the pathogenesis and treatment of diseases. However, because of the limitation of time-consuming and expensive experimental methods, predicting miRNA-disease associations by computational models has become a more economical and effective mean. Results Inspired by the work of predecessors, we proposed an improved computational model based on random forest (RF) for identifying miRNA-disease associations (IRFMDA). First, the integrated similarity of diseases and the integrated similarity of miRNAs were calculated by combining the semantic similarity and Gaussian interaction profile kernel (GIPK) similarity of diseases, the functional similarity and GIPK similarity of miRNAs, respectively. Then, the integrated similarity of diseases and the integrated similarity of miRNAs were combined to represent each miRNA-disease relationship pair. Next, the miRNA-disease relationship pairs contained in the HMDD (v2.0) database were considered positive samples, and the randomly constructed miRNA-disease relationship pairs not included in HMDD (v2.0) were considered negative samples. Next, the feature selection based on the variable importance score of RF was performed to choose more useful features to represent samples to optimize the model’s ability of inferring miRNA-disease associations. Finally, a RF regression model was trained on reduced sample space to score the unknown miRNA-disease associations. The AUCs of IRFMDA under local leave-one-out cross-validation (LOOCV), global LOOCV and 5-fold cross-validation achieved 0.8728, 0.9398 and 0.9363, which were better than several excellent models for predicting miRNA-disease associations. Moreover, case studies on oesophageal cancer, lymphoma and lung cancer showed that 94 (oesophageal cancer), 98 (lymphoma) and 100 (lung cancer) of the top 100 disease-associated miRNAs predicted by IRFMDA were supported by the experimental data in the dbDEMC (v2.0) database. Conclusions Cross-validation and case studies demonstrated that IRFMDA is an excellent miRNA-disease association prediction model, and can provide guidance and help for experimental studies on the regulatory mechanism of miRNAs in complex human diseases in the future.
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71
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Prediction of potential miRNA-disease associations using matrix decomposition and label propagation. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.104963] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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72
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Wang J, Zhang J, Cai Y, Deng L. DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model. Int J Mol Sci 2019; 20:E6046. [PMID: 31801264 PMCID: PMC6928926 DOI: 10.3390/ijms20236046] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 11/25/2019] [Accepted: 11/26/2019] [Indexed: 01/08/2023] Open
Abstract
MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.
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Affiliation(s)
- Jiacheng Wang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; (J.W.); (Y.C.)
| | - Jingpu Zhang
- School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan 467000, China;
| | - Yideng Cai
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; (J.W.); (Y.C.)
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China; (J.W.); (Y.C.)
- School of Software, Xinjiang University, Urumqi 830008, China
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73
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Guan NN, Wang CC, Zhang L, Huang L, Li JQ, Piao X. In silico prediction of potential miRNA-disease association using an integrative bioinformatics approach based on kernel fusion. J Cell Mol Med 2019; 24:573-587. [PMID: 31747722 PMCID: PMC6933403 DOI: 10.1111/jcmm.14765] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 08/13/2019] [Accepted: 09/20/2019] [Indexed: 12/18/2022] Open
Abstract
Accumulating experimental evidence has demonstrated that microRNAs (miRNAs) have a huge impact on numerous critical biological processes and they are associated with different complex human diseases. Nevertheless, the task to predict potential miRNAs related to diseases remains difficult. In this paper, we developed a Kernel Fusion-based Regularized Least Squares for MiRNA-Disease Association prediction model (KFRLSMDA), which applied kernel fusion technique to fuse similarity matrices and then utilized regularized least squares to predict potential miRNA-disease associations. To prove the effectiveness of KFRLSMDA, we adopted leave-one-out cross-validation (LOOCV) and 5-fold cross-validation and then compared KFRLSMDA with 10 previous computational models (MaxFlow, MiRAI, MIDP, RKNNMDA, MCMDA, HGIMDA, RLSMDA, HDMP, WBSMDA and RWRMDA). Outperforming other models, KFRLSMDA achieved AUCs of 0.9246 in global LOOCV, 0.8243 in local LOOCV and average AUC of 0.9175 ± 0.0008 in 5-fold cross-validation. In addition, respectively, 96%, 100% and 90% of the top 50 potential miRNAs for breast neoplasms, colon neoplasms and oesophageal neoplasms were confirmed by experimental discoveries. We also predicted potential miRNAs related to hepatocellular cancer by removing all known related miRNAs of this cancer and 98% of the top 50 potential miRNAs were verified. Furthermore, we predicted potential miRNAs related to lymphoma using the data set in the old version of the HMDD database and 80% of the top 50 potential miRNAs were confirmed. Therefore, it can be concluded that KFRLSMDA has reliable prediction performance.
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Affiliation(s)
- Na-Na Guan
- College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang, China.,College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, China.,The Future Laboratory, Tsinghua University, Beijing, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Xue Piao
- School of Medical Informatics, Xuzhou Medical University, Xuzhou, China
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74
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Pan X, Shen HB. Inferring Disease-Associated MicroRNAs Using Semi-supervised Multi-Label Graph Convolutional Networks. iScience 2019; 20:265-277. [PMID: 31605942 PMCID: PMC6817654 DOI: 10.1016/j.isci.2019.09.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 09/05/2019] [Accepted: 09/11/2019] [Indexed: 01/22/2023] Open
Abstract
MicroRNAs (miRNAs) play crucial roles in biological processes involved in diseases. The associations between diseases and protein-coding genes (PCGs) have been well investigated, and miRNAs interact with PCGs to trigger them to be functional. We present a computational method, DimiG, to infer miRNA-associated diseases using a semi-supervised Graph Convolutional Network model (GCN). DimiG uses a multi-label framework to integrate PCG-PCG interactions, PCG-miRNA interactions, PCG-disease associations, and tissue expression profiles. DimiG is trained on disease-PCG associations and an interaction network using a GCN, which is further used to score associations between diseases and miRNAs. We evaluate DimiG on a benchmark set from verified disease-miRNA associations. Our results demonstrate that DimiG outperforms the best unsupervised method and is comparable to two supervised methods. Three case studies of prostate cancer, lung cancer, and inflammatory bowel disease further demonstrate the efficacy of DimiG, where top miRNAs predicted by DimiG are supported by literature.
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Affiliation(s)
- Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China; Department of Medical informatics, Erasmus Medical Center, 3015 CE Rotterdam, the Netherlands.
| | - Hong-Bin Shen
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, 200240 Shanghai, China.
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75
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Chen X, Xie D, Wang L, Zhao Q, You ZH, Liu H. BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction. Bioinformatics 2019; 34:3178-3186. [PMID: 29701758 DOI: 10.1093/bioinformatics/bty333] [Citation(s) in RCA: 235] [Impact Index Per Article: 39.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2017] [Accepted: 04/24/2018] [Indexed: 12/16/2022] Open
Abstract
Motivation A large number of resources have been devoted to exploring the associations between microRNAs (miRNAs) and diseases in the recent years. However, the experimental methods are expensive and time-consuming. Therefore, the computational methods to predict potential miRNA-disease associations have been paid increasing attention. Results In this paper, we proposed a novel computational model of Bipartite Network Projection for MiRNA-Disease Association prediction (BNPMDA) based on the known miRNA-disease associations, integrated miRNA similarity and integrated disease similarity. We firstly described the preference degree of a miRNA for its related disease and the preference degree of a disease for its related miRNA with the bias ratings. We constructed bias ratings for miRNAs and diseases by using agglomerative hierarchical clustering according to the three types of networks. Then, we implemented the bipartite network recommendation algorithm to predict the potential miRNA-disease associations by assigning transfer weights to resource allocation links between miRNAs and diseases based on the bias ratings. BNPMDA had been shown to improve the prediction accuracy in comparison with previous models according to the area under the receiver operating characteristics (ROC) curve (AUC) results of three typical cross validations. As a result, the AUCs of Global LOOCV, Local LOOCV and 5-fold cross validation obtained by implementing BNPMDA were 0.9028, 0.8380 and 0.8980 ± 0.0013, respectively. We further implemented two types of case studies on several important human complex diseases to confirm the effectiveness of BNPMDA. In conclusion, BNPMDA could effectively predict the potential miRNA-disease associations at a high accuracy level. Availability and implementation BNPMDA is available via http://www.escience.cn/system/file?fileId=99559. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Di Xie
- School of Mathematics, Liaoning University, Shenyang, China
| | - Lei Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Qi Zhao
- School of Mathematics, Liaoning University, Shenyang, China.,Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi, China
| | - Hongsheng Liu
- Research Center for Computer Simulating and Information Processing of Bio-Macromolecules of Liaoning Province, Shenyang, China.,School of Life Science, Liaoning University, Shenyang, China
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76
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Chen X, Wang L, Qu J, Guan NN, Li JQ. Predicting miRNA-disease association based on inductive matrix completion. Bioinformatics 2019; 34:4256-4265. [PMID: 29939227 DOI: 10.1093/bioinformatics/bty503] [Citation(s) in RCA: 273] [Impact Index Per Article: 45.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2018] [Accepted: 06/20/2018] [Indexed: 12/17/2022] Open
Abstract
Motivation It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement. Results This paper presents a novel model of Inductive Matrix Completion for MiRNA-Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA-disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified. Availability and implementation The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Lei Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jia Qu
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Na-Na Guan
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Jian-Qiang Li
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
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Zhang L, Chen X, Yin J. Prediction of Potential miRNA-Disease Associations Through a Novel Unsupervised Deep Learning Framework with Variational Autoencoder. Cells 2019; 8:cells8091040. [PMID: 31489920 PMCID: PMC6770222 DOI: 10.3390/cells8091040] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2019] [Revised: 08/31/2019] [Accepted: 09/02/2019] [Indexed: 12/22/2022] Open
Abstract
The important role of microRNAs (miRNAs) in the formation, development, diagnosis, and treatment of diseases has attracted much attention among researchers recently. In this study, we present an unsupervised deep learning model of the variational autoencoder for MiRNA–disease association prediction (VAEMDA). Through combining the integrated miRNA similarity and the integrated disease similarity with known miRNA–disease associations, respectively, we constructed two spliced matrices. These matrices were applied to train the variational autoencoder (VAE), respectively. The final predicted association scores between miRNAs and diseases were obtained by integrating the scores from the two trained VAE models. Unlike previous models, VAEMDA can avoid noise introduced by the random selection of negative samples and reveal associations between miRNAs and diseases from the perspective of data distribution. Compared with previous methods, VAEMDA obtained higher area under the receiver operating characteristics curves (AUCs) of 0.9118, 0.8652, and 0.9091 ± 0.0065 in global leave-one-out cross validation (LOOCV), local LOOCV, and five-fold cross validation, respectively. Further, the AUCs of VAEMDA were 0.8250 and 0.8237 in global leave-one-disease-out cross validation (LODOCV), and local LODOCV, respectively. In three different types of case studies on three important diseases, the results showed that most of the top 50 potentially associated miRNAs were verified by databases and the literature.
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Affiliation(s)
- Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
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78
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Chen Q, Zhao Z, Lan W, Zhang R, Liang J. Predicting miRNA-disease interaction based on recommend method. INFORMATION DISCOVERY AND DELIVERY 2019. [DOI: 10.1108/idd-04-2019-0026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Purpose
MicroRNAs (miRNAs) have been proved to be a significant type of non-coding RNAs related to various human diseases. This paper aims to identify the potential miRNA–disease interactions.
Design/methodology/approach
A computational framework, MDIRM is presented to predict miRNAs-disease interactions. Unlike traditional approaches, the miRNA function similarity is calculated by miRNA–disease interactions. The k-mean method is further used to cluster miRNA similarity network. For miRNAs in the same cluster, their similarities are enhanced, as the miRNAs from the same cluster may be reliable. Further, the potential miRNA–disease association is predicted by using recommend method.
Findings
To evaluate the performance of our model, the fivefold cross validation is implemented to compare with two state-of-the-art methods. The experimental results indicate that MDIRM achieves an AUC of 0.926, which outperforms other methods.
Originality/value
This paper proposes a novel computational method for miRNA–disease interaction prediction based on recommend method. Identifying the relationship between miRNAs and diseases not only helps us better understand the disease occurrence and mechanism through the perspective of miRNA but also promotes disease diagnosis and treatment.
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79
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Chen X, Xie D, Zhao Q, You ZH. MicroRNAs and complex diseases: from experimental results to computational models. Brief Bioinform 2019; 20:515-539. [PMID: 29045685 DOI: 10.1093/bib/bbx130] [Citation(s) in RCA: 427] [Impact Index Per Article: 71.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 08/13/2017] [Indexed: 12/22/2022] Open
Abstract
Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Di Xie
- School of Mathematics, Liaoning University
| | - Qi Zhao
- School of Mathematics, Liaoning University
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science
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80
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Wiczling P, Daghir-Wojtkowiak E, Kaliszan R, Markuszewski MJ, Limon J, Koczkowska M, Stukan M, Kuźniacka A, Ratajska M. Bayesian multilevel model of micro RNA levels in ovarian-cancer and healthy subjects. PLoS One 2019; 14:e0221764. [PMID: 31465488 PMCID: PMC6715278 DOI: 10.1371/journal.pone.0221764] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 08/14/2019] [Indexed: 12/31/2022] Open
Abstract
In transcriptomics, micro RNAs (miRNAs) has gained much interest especially as potential disease indicators. However, apart from holding a great promise related to their clinical application, a lot of inconsistent results have been published. Our aim was to compare the miRNA expression levels in ovarian cancer and healthy subjects using the Bayesian multilevel model and to assess their potential usefulness in diagnosis. We have analyzed a case-control observational data on expression profiling of 49 preselected miRNA-based ovarian cancer indicators in 119 controls and 59 patients. A Bayesian multilevel model was used to characterize the effect of disease on miRNA levels controlling for differences in age and body weight. The difference between the miRNA level and health status of the patient on the scale of the data variability were discussed in the context of their potential usefulness in diagnosis. Additionally, the cross-validated area under the ROC curve (AUC) was used to assess the expected out-of-sample discrimination index of a different sets of miRNAs. The proposed model allowed us to describe the set of miRNA levels in patients and controls. Three highly correlated miRNAs: miR-101-3p, miR-142-5p, miR-148a-3p rank the highest with almost identical effect sizes that ranges from 0.45 to 1.0. For those miRNAs the credible interval for AUC ranged from 0.63 to 0.67 indicating their limited discrimination potential. A little benefit in adding information from other miRNAs was observed. There were several miRNAs in the dataset (miR-604, hsa-miR-221-5p) for which inferences were uncertain. For those miRNAs more experimental effort is needed to fully assess their effect in the context of new hits discovery and usefulness as disease indicators. The proposed multilevel Bayesian model can be used to characterize the panel of miRNA profile and to assess the difference in expression levels between healthy and cancer individuals.
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Affiliation(s)
- Paweł Wiczling
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera, Gdańsk, Poland
| | - Emilia Daghir-Wojtkowiak
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera, Gdańsk, Poland
| | - Roman Kaliszan
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera, Gdańsk, Poland
| | - Michał Jan Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera, Gdańsk, Poland
| | - Janusz Limon
- Department of Biology and Genetics, Medical University of Gdańsk, Dębinki, Gdańsk, Poland
| | - Magdalena Koczkowska
- Department of Biology and Genetics, Medical University of Gdańsk, Dębinki, Gdańsk, Poland
| | - Maciej Stukan
- Department of Gynecological Oncology, Gdynia Oncology Centre, Powstania Styczniowego, Gdynia, Poland
| | - Alina Kuźniacka
- Department of Biology and Genetics, Medical University of Gdańsk, Dębinki, Gdańsk, Poland
| | - Magdalena Ratajska
- Department of Biology and Genetics, Medical University of Gdańsk, Dębinki, Gdańsk, Poland
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81
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Identifying MiRNA-disease association based on integrating miRNA topological similarity and functional similarity. QUANTITATIVE BIOLOGY 2019. [DOI: 10.1007/s40484-019-0176-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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82
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Zheng K, You ZH, Wang L, Zhou Y, Li LP, Li ZW. MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources. J Transl Med 2019; 17:260. [PMID: 31395072 PMCID: PMC6688360 DOI: 10.1186/s12967-019-2009-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 07/31/2019] [Indexed: 02/01/2023] Open
Abstract
Background Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease. Methods In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA–disease associations. Results The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate the prediction performance of MLMDA model, case studies are carried out with three Human complex diseases including Lymphoma, Lung Neoplasm, and Esophageal Neoplasms. As a result, 39, 37 and 36 out of the top 40 predicted miRNAs are confirmed by other miRNA–disease association databases. Conclusions These prominent experimental results suggest that the MLMDA model could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. The source code and datasets explored in this work are available at http://220.171.34.3:81/.
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Affiliation(s)
- Kai Zheng
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Zhu-Hong You
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, 830011, China.
| | - Lei Wang
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, 830011, China. .,College of Information Science and Engineering, Zaozhuang University, Zaozhuang, 277100, China.
| | - Yong Zhou
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
| | - Li-Ping Li
- Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, 830011, China
| | - Zheng-Wei Li
- School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China
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83
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Chen H, Zhang Z, Feng D. Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis. BMC Bioinformatics 2019; 20:404. [PMID: 31345171 PMCID: PMC6657378 DOI: 10.1186/s12859-019-2998-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 07/16/2019] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups. RESULTS In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases. CONCLUSIONS The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.
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Affiliation(s)
- Hailin Chen
- School of Software, East China Jiaotong University, Nanchang, 330013 China
| | - Zuping Zhang
- School of Computer Science and Engineering, Central South University, Changsha, 410083 China
| | - Dayi Feng
- School of Software, East China Jiaotong University, Nanchang, 330013 China
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84
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Ensemble of decision tree reveals potential miRNA-disease associations. PLoS Comput Biol 2019; 15:e1007209. [PMID: 31329575 PMCID: PMC6675125 DOI: 10.1371/journal.pcbi.1007209] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 08/01/2019] [Accepted: 06/24/2019] [Indexed: 12/14/2022] Open
Abstract
In recent years, increasing associations between microRNAs (miRNAs) and human diseases have been identified. Based on accumulating biological data, many computational models for potential miRNA-disease associations inference have been developed, which saves time and expenditure on experimental studies, making great contributions to researching molecular mechanism of human diseases and developing new drugs for disease treatment. In this paper, we proposed a novel computational method named Ensemble of Decision Tree based MiRNA-Disease Association prediction (EDTMDA), which innovatively built a computational framework integrating ensemble learning and dimensionality reduction. For each miRNA-disease pair, the feature vector was extracted by calculating the statistical measures, graph theoretical measures, and matrix factorization results for the miRNA and disease, respectively. Then multiple base learnings were built to yield many decision trees (DTs) based on random selection of negative samples and miRNA/disease features. Particularly, Principal Components Analysis was applied to each base learning to reduce feature dimensionality and hence remove the noise or redundancy. Average strategy was adopted for these DTs to get final association scores between miRNAs and diseases. In model performance evaluation, EDTMDA showed AUC of 0.9309 in global leave-one-out cross validation (LOOCV) and AUC of 0.8524 in local LOOCV. Additionally, AUC of 0.9192+/-0.0009 in 5-fold cross validation proved the model's reliability and stability. Furthermore, three types of case studies for four human diseases were implemented. As a result, 94% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 96% (Breast Neoplasms) and 88% (Carcinoma Hepatocellular) of top 50 predicted miRNAs were confirmed by experimental evidences in literature.
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85
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Huang J, Li X, Liu P, Wang J, Li H. Retracted: Emodin protects H9c2 cells against hypoxia-induced injury via regulation of miR-26a/survivin and the JAK1/STAT3 pathway. J Cell Biochem 2019; 120:11081-11090. [PMID: 30701574 DOI: 10.1002/jcb.28385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2018] [Accepted: 01/10/2019] [Indexed: 02/02/2023]
Abstract
BACKGROUND/AIM Congenital heart disease (CHD) is a catastrophic disease. Emodin possesses biological properties in protecting against some diseases. Our study investigated to explore the effects of emodin on hypoxia-stimulated cardiomyocytes, which mimicked CHD in vitro. METHODS H9c2 cells were stimulated with hypoxia and then the cells were treated with or without emodin, and/or transfected with miR-26a mimic, pcDNA-survivin and their corresponding negative control (NC). Cell viability and cell apoptosis were detected by Cell Counting kit-8 assay and flow cytometry, respectively. In addition, the expression of apoptotic proteins, Janus kinase 1 (JNK)/signal transducer and activator of transcription 3 (STAT3) pathway factors, and survivin were evaluated by using Western blot analysis. The expression of miR-26a was analyzed by quantitative real time polymerase chain reaction (qRT-PCR). Moreover, the target of miR-26a was verified by using a luciferase report assay. RESULTS Hypoxia significantly decreased cell viability and increased cell apoptosis, and the accumulated levels of cleaved caspase-3 and cleaved-caspase-9 were upregulated by hypoxia compared with the control. However, emodin administration led to the opposite result. A further result showed that emodin increased the phosphorylation of JNK/STAT3 pathway-related proteins and the pathway inhibitor AG490 impaired the protective effects of emodin on hypoxia-induced injury. In addition, emodin negatively regulated the miR-26a expression, and overexpression of miR-26a enhanced cell apoptosis and upregulated the expression of cleaved-caspase-3 and cleaved-caspase-9 compared with the NC. Moreover, emodin statistically upregulated the expression of survivin, and overexpression of miR-26a decreased the expression of survivin. The luciferase of miR-26a overexpression was decreased in the wild type of the survivin group. CONCLUSION Emodin protects hypoxia-induced cell injury as evidenced by increasing cell viability and decreasing apoptosis through downregulation of miR-26a as well as activation of the JNK/STAT3 pathway.
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Affiliation(s)
- Jiancheng Huang
- Department of Congenital Heart Disease, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xiaobing Li
- Department of Congenital Heart Disease, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Pujuan Liu
- Department of Congenital Heart Disease, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Jun Wang
- Department of Congenital Heart Disease, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Hongying Li
- Department of Congenital Heart Disease, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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86
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Yan F, Zheng Y, Jia W, Hou S, Xiao R. MAMDA: Inferring microRNA-Disease associations with manifold alignment. Comput Biol Med 2019; 110:156-163. [DOI: 10.1016/j.compbiomed.2019.05.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 05/17/2019] [Accepted: 05/17/2019] [Indexed: 01/13/2023]
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87
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Gao YL, Cui Z, Liu JX, Wang J, Zheng CH. NPCMF: Nearest Profile-based Collaborative Matrix Factorization method for predicting miRNA-disease associations. BMC Bioinformatics 2019; 20:353. [PMID: 31234797 PMCID: PMC6591872 DOI: 10.1186/s12859-019-2956-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 06/17/2019] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Predicting meaningful miRNA-disease associations (MDAs) is costly. Therefore, an increasing number of researchers are beginning to focus on methods to predict potential MDAs. Thus, prediction methods with improved accuracy are under development. An efficient computational method is proposed to be crucial for predicting novel MDAs. For improved experimental productivity, large biological datasets are used by researchers. Although there are many effective and feasible methods to predict potential MDAs, the possibility remains that these methods are flawed. RESULTS A simple and effective method, known as Nearest Profile-based Collaborative Matrix Factorization (NPCMF), is proposed to identify novel MDAs. The nearest profile is introduced to our method to achieve the highest AUC value compared with other advanced methods. For some miRNAs and diseases without any association, we use the nearest neighbour information to complete the prediction. CONCLUSIONS To evaluate the performance of our method, five-fold cross-validation is used to calculate the AUC value. At the same time, three disease cases, gastric neoplasms, rectal neoplasms and colonic neoplasms, are used to predict novel MDAs on a gold-standard dataset. We predict the vast majority of known MDAs and some novel MDAs. Finally, the prediction accuracy of our method is determined to be better than that of other existing methods. Thus, the proposed prediction model can obtain reliable experimental results.
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Affiliation(s)
- Ying-Lian Gao
- Library of Qufu Normal University, Qufu Normal University, Rizhao, China
| | - Zhen Cui
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Jin-Xing Liu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China. .,Co-Innovation Center for Information Supply and Assurance Technology, Anhui University, Hefei, China.
| | - Juan Wang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, China
| | - Chun-Hou Zheng
- Co-Innovation Center for Information Supply and Assurance Technology, Anhui University, Hefei, China
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88
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Tang C, Zhou H, Zheng X, Zhang Y, Sha X. Dual Laplacian regularized matrix completion for microRNA-disease associations prediction. RNA Biol 2019; 16:601-611. [PMID: 30676207 PMCID: PMC6546388 DOI: 10.1080/15476286.2019.1570811] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Revised: 11/30/2018] [Accepted: 01/03/2019] [Indexed: 01/21/2023] Open
Abstract
Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNA-disease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNA-disease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNA-disease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.
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Affiliation(s)
- Chang Tang
- School of Computer Science, China University of Geosciences, Wuhan, China
| | - Hua Zhou
- Department of Hematology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, China
| | - Xiao Zheng
- Wuhan University of Technology Hospital, Wuhan University of Technology, Wuhan, China
| | - Yanming Zhang
- Department of Hematology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, China
| | - Xiaofeng Sha
- Department of Oncology, Huai’an Hongze District People’s Hospital, Huai’an, China
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89
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Zhao Y, Chen X, Yin J. Adaptive boosting-based computational model for predicting potential miRNA-disease associations. Bioinformatics 2019; 35:4730-4738. [DOI: 10.1093/bioinformatics/btz297] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 03/19/2019] [Accepted: 04/18/2019] [Indexed: 12/24/2022] Open
Abstract
AbstractMotivationRecent studies have shown that microRNAs (miRNAs) play a critical part in several biological processes and dysregulation of miRNAs is related with numerous complex human diseases. Thus, in-depth research of miRNAs and their association with human diseases can help us to solve many problems.ResultsDue to the high cost of traditional experimental methods, revealing disease-related miRNAs through computational models is a more economical and efficient way. Considering the disadvantages of previous models, in this paper, we developed adaptive boosting for miRNA-disease association prediction (ABMDA) to predict potential associations between diseases and miRNAs. We balanced the positive and negative samples by performing random sampling based on k-means clustering on negative samples, whose process was quick and easy, and our model had higher efficiency and scalability for large datasets than previous methods. As a boosting technology, ABMDA was able to improve the accuracy of given learning algorithm by integrating weak classifiers that could score samples to form a strong classifier based on corresponding weights. Here, we used decision tree as our weak classifier. As a result, the area under the curve (AUC) of global and local leave-one-out cross validation reached 0.9170 and 0.8220, respectively. What is more, the mean and the standard deviation of AUCs achieved 0.9023 and 0.0016, respectively in 5-fold cross validation. Besides, in the case studies of three important human cancers, 49, 50 and 50 out of the top 50 predicted miRNAs for colon neoplasms, hepatocellular carcinoma and breast neoplasms were confirmed by the databases and experimental literatures.Availability and implementationThe code and dataset of ABMDA are freely available at https://github.com/githubcode007/ABMDA.Supplementary informationSupplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
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90
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FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs. BMC SYSTEMS BIOLOGY 2019; 13:26. [PMID: 30953512 PMCID: PMC6449885 DOI: 10.1186/s12918-019-0696-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Background Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments. Methods In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association score between miRNA and disease. Information about miRNA cluster is also used to improve prediction accuracy. Result FCMDAP achieves an average AUC of 0.9165 based on leave-one-out cross validation. Results confirm the 100, 98 and 96% of the top 50 predicted miRNAs reported in case studies on colorectal, lung, and pancreatic neoplasms. FCMDAP also exhibits satisfactory performance in predicting diseases without any related miRNAs and miRNAs without any related diseases. Conclusions In this study, we present a computational method FCMDAP to improve the prediction accuracy of disease related miRNAs. FCMDAP could be an effective tool for further biological experiments. Electronic supplementary material The online version of this article (10.1186/s12918-019-0696-9) contains supplementary material, which is available to authorized users.
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91
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Wang L, You ZH, Chen X, Li YM, Dong YN, Li LP, Zheng K. LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities. PLoS Comput Biol 2019; 15:e1006865. [PMID: 30917115 PMCID: PMC6464243 DOI: 10.1371/journal.pcbi.1006865] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2018] [Revised: 04/15/2019] [Accepted: 02/13/2019] [Indexed: 11/18/2022] Open
Abstract
Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases.
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Affiliation(s)
- Lei Wang
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
- * E-mail: (ZHY); (XC)
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
- * E-mail: (ZHY); (XC)
| | - Yang-Ming Li
- Department of Electrical Computer and Telecommunications Engineering Technology, Rochester Institute of Technology, Rochester, United States of America
| | - Ya-Nan Dong
- Xiangya School of Public Health, Central South University, Changsha, China
| | - Li-Ping Li
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
| | - Kai Zheng
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China
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92
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Integrating random walk and binary regression to identify novel miRNA-disease association. BMC Bioinformatics 2019; 20:59. [PMID: 30691413 PMCID: PMC6350368 DOI: 10.1186/s12859-019-2640-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 01/18/2019] [Indexed: 02/07/2023] Open
Abstract
Background In the last few decades, cumulative experimental researches have witnessed and verified the important roles of microRNAs (miRNAs) in the development of human complex diseases. Benefitting from the rapid growth both in the availability of miRNA-related data and the development of various analysis methodologies, up until recently, some computational models have been developed to predict human disease related miRNAs, efficiently and quickly. Results In this work, we proposed a computational model of Random Walk and Binary Regression-based MiRNA-Disease Association prediction (RWBRMDA). RWBRMDA extracted features for each miRNA from random walk with restart on the integrated miRNA similarity network for binary logistic regression to predict potential miRNA-disease associations. RWBRMDA obtained AUC of 0.8076 in the leave-one-out cross validation. Additionally, we carried out three different patterns of case studies on four human complex diseases. Specifically, Esophageal cancer and Prostate cancer were conducted as one kind of case study based on known miRNA-disease associations in HMDD v2.0 database. Out of the top 50 predicted miRNAs, 94 and 90% were respectively confirmed by recent experimental reports. To simulate new disease without known related miRNAs, the information of known Breast cancer related miRNAs was removed. As a result, 98% of the top 50 predicted miRNAs for Breast cancer were confirmed. Lymphoma, the verified ratio of which was 88%, was used to assess the prediction robustness of RWBRMDA based on the association records in HMDD v1.0 database. Conclusions We anticipated that RWBRMDA could benefit the future experimental investigations about the relation between human disease and miRNAs by generating promising and testable top-ranked miRNAs, and significantly reducing the effort and cost of identification works. Electronic supplementary material The online version of this article (10.1186/s12859-019-2640-9) contains supplementary material, which is available to authorized users.
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93
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Che K, Guo M, Wang C, Liu X, Chen X. Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples. Genes (Basel) 2019; 10:genes10020080. [PMID: 30682853 PMCID: PMC6410147 DOI: 10.3390/genes10020080] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 01/18/2019] [Accepted: 01/18/2019] [Indexed: 11/16/2022] Open
Abstract
In discovering disease etiology and pathogenesis, the associations between MicroRNAs (miRNAs) and diseases play a critical role. Given known miRNA-disease associations (MDAs), how to uncover potential MDAs is an important problem. To solve this problem, most of the existing methods regard known MDAs as positive samples and unknown ones as negative samples, and then predict possible MDAs by iteratively revising the negative samples. However, simply viewing unknown MDAs as negative samples introduces erroneous information, which may result in poor predication performance. To avoid such defects, we present a novel method using only positive samples to predict MDAs by latent features extraction (LFEMDA). We design a new approach to construct the miRNAs similarity matrix. LFEMDA integrates the disease similarity matrix, the known MDAs and the miRNAs similarity matrix to identify potential MDAs. By introducing miRNAs and diseases knowledge as the auxiliary variables, the method can converge to give the optimal solution in each iteration. We conduct experiments on high-association diseases and new diseases datasets, in which our method shows better performance than that of other methods. We also carry out a case study on breast neoplasms to further demonstrate the capacity of our method in uncovering potential MDAs.
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Affiliation(s)
- Kai Che
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
| | - Maozu Guo
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
- School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China.
- Beijing Key Laboratory of Intelligent Processing for Building Big Data, Beijing 100044, China.
| | - Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
| | - Xiaoyan Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
| | - Xi Chen
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
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94
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Sun Y, Zhu Z, You ZH, Zeng Z, Huang ZA, Huang YA. FMSM: a novel computational model for predicting potential miRNA biomarkers for various human diseases. BMC SYSTEMS BIOLOGY 2018; 12:121. [PMID: 30598090 PMCID: PMC6311922 DOI: 10.1186/s12918-018-0664-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background MicroRNA (miRNA) plays a key role in regulation mechanism of human biological processes, including the development of disease and disorder. It is necessary to identify potential miRNA biomarkers for various human diseases. Computational prediction model is expected to accelerate the process of identification. Results Considering the limitations of previously proposed models, we present a novel computational model called FMSM. It infers latent miRNA biomarkers involved in the mechanism of various diseases based on the known miRNA-disease association network, miRNA expression similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. FMSM achieves reliable prediction performance in 5-fold and leave-one-out cross validations with area under ROC curve (AUC) values of 0.9629+/− 0.0127 and 0.9433, respectively, which outperforms the state-of-the-art competitors and classical algorithms. In addition, 19 of top 25 predicted miRNAs have been validated to have associations with Colonic Neoplasms in case study. Conclusions A factored miRNA similarity based model and miRNA expression similarity substantially contribute to the well-performing prediction. The list of the predicted most latent miRNA biomarkers of various human diseases is publicized. It is anticipated that FMSM could serve as a useful tool guiding the future experimental validation for those promising miRNA biomarker candidates. Electronic supplementary material The online version of this article (10.1186/s12918-018-0664-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yiwen Sun
- School of Medicine, Shenzhen University, Shenzhen, 518060, China
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, ürümqi, 830011, China
| | - Zijie Zeng
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Zhi-An Huang
- Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China.
| | - Yu-An Huang
- Department of Computing, Hong Kong Polytechnic University, Hong Kong, 999077, China.
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95
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Jiang L, Xiao Y, Ding Y, Tang J, Guo F. FKL-Spa-LapRLS: an accurate method for identifying human microRNA-disease association. BMC Genomics 2018; 19:911. [PMID: 30598109 PMCID: PMC6311941 DOI: 10.1186/s12864-018-5273-x] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the process of post-transcription, microRNAs (miRNAs) are closely related to various complex human diseases. Traditional verification methods for miRNA-disease associations take a lot of time and expense, so it is especially important to design computational methods for detecting potential associations. Considering the restrictions of previous computational methods for predicting potential miRNAs-disease associations, we develop the model of FKL-Spa-LapRLS (Fast Kernel Learning Sparse kernel Laplacian Regularized Least Squares) to break through the limitations. RESULT First, we extract three miRNA similarity kernels and three disease similarity kernels. Then, we combine these kernels into a single kernel through the Fast Kernel Learning (FKL) model, and use sparse kernel (Spa) to eliminate noise in the integrated similarity kernel. Finally, we find the associations via Laplacian Regularized Least Squares (LapRLS). Based on three evaluation methods, global and local leave-one-out cross validation (LOOCV), and 5-fold cross validation, the AUCs of our method achieve 0.9563, 0.8398 and 0.9535, thus it can be seen that our method is reliable. Then, we use case studies of eight neoplasms to further analyze the performance of our method. We find that most of the predicted miRNA-disease associations are confirmed by previous traditional experiments, and some important miRNAs should be paid more attention, which uncover more associations of various neoplasms than other miRNAs. CONCLUSIONS Our proposed model can reveal miRNA-disease associations and improve the accuracy of correlation prediction for various diseases. Our method can be also easily extended with more similarity kernels.
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Affiliation(s)
- Limin Jiang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Tianjin University Institute of Computational Biology, Tianjin University, Tianjin, China
| | - Yongkang Xiao
- School of Chemical Engineering and Technology, Tianjin University, Tianjin, China
| | - Yijie Ding
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Jijun Tang
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.,Tianjin University Institute of Computational Biology, Tianjin University, Tianjin, China.,Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA
| | - Fei Guo
- School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.
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96
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Liang C, Yu S, Wong KC, Luo J. A novel semi-supervised model for miRNA-disease association prediction based on
ℓ
1
-norm graph. J Transl Med 2018; 16:357. [PMID: 30547813 PMCID: PMC6295065 DOI: 10.1186/s12967-018-1741-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 12/10/2018] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Identification of miRNA-disease associations has attracted much attention recently due to the functional roles of miRNAs implicated in various biological and pathological processes. Great efforts have been made to discover the potential associations between miRNAs and diseases both experimentally and computationally. Although reliable, the experimental methods are in general time-consuming and labor-intensive. In comparison, computational methods are more efficient and applicable to large-scale datasets. METHODS In this paper, we propose a novel semi-supervised model to predict miRNA-disease associations viaℓ 1 -norm graph. Specifically, we first recalculate the miRNA functional similarities as well as the disease semantic similarities based on the latest version of MeSH descriptors and HMDD. We then update the similarity matrices and association matrix iteratively in both miRNA space and disease space. The optimized association matrices from each space are combined together as the final output. RESULTS Compared with four state-of-the-art prediction methods, our method achieved favorable performance with AUCs of 0.943 and 0.946 in both global LOOCV and local LOOCV, respectively. In addition, we carried out three types of case studies on five common human diseases, and most of the top 50 predicted miRNAs were confirmed to be associated with the investigated diseases by four databases dbDEMC, PheomiR, miR2Disease and miRwayDB. Specifically, our results provided potential evidence that miRNAs within the same family or cluster were likely to play functional roles together in given diseases. CONCLUSIONS Taken together, the experimental results clearly demonstrated the utility of the proposed method. We anticipated that our method could serve as a reliable and efficient tool for miRNA-disease association prediction.
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Affiliation(s)
- Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358 China
| | - Shengpeng Yu
- School of Information Science and Engineering, Shandong Normal University, Jinan, 250358 China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, 999077 Hong Kong
| | - Jiawei Luo
- College of Information Science and Engineering, Hunan University, Changsha, 410082 China
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97
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Chen X, Zhang DH, You ZH. A heterogeneous label propagation approach to explore the potential associations between miRNA and disease. J Transl Med 2018; 16:348. [PMID: 30537965 PMCID: PMC6290528 DOI: 10.1186/s12967-018-1722-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Accepted: 12/04/2018] [Indexed: 02/06/2023] Open
Abstract
Background Research on microRNAs (miRNAs) has attracted increasingly worldwide attention over recent years as growing experimental results have made clear that miRNA correlates with masses of critical biological processes and the occurrence, development, and diagnosis of human complex diseases. Nonetheless, the known miRNA-disease associations are still insufficient considering plenty of human miRNAs discovered now. Therefore, there is an urgent need for effective computational model predicting novel miRNA-disease association prediction to save time and money for follow-up biological experiments. Methods In this study, considering the insufficiency of the previous computational methods, we proposed the model named heterogeneous label propagation for MiRNA-disease association prediction (HLPMDA), in which a heterogeneous label was propagated on the multi-network of miRNA, disease and long non-coding RNA (lncRNA) to infer the possible miRNA-disease association. The strength of the data about lncRNA–miRNA association and lncRNA-disease association enabled HLPMDA to produce a better prediction. Results HLPMDA achieved AUCs of 0.9232, 0.8437 and 0.9218 ± 0.0004 based on global and local leave-one-out cross validation and 5-fold cross validation, respectively. Furthermore, three kinds of case studies were implemented and 47 (esophageal neoplasms), 49 (breast neoplasms) and 46 (lymphoma) of top 50 candidate miRNAs were proved by experiment reports. Conclusions All the results adequately showed that HLPMDA is a recommendable miRNA-disease association prediction method. We anticipated that HLPMDA could help the follow-up investigations by biomedical researchers. Electronic supplementary material The online version of this article (10.1186/s12967-018-1722-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
| | - De-Hong Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi, 830011, China.
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98
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Chen X, Wang CC, Yin J, You ZH. Novel Human miRNA-Disease Association Inference Based on Random Forest. MOLECULAR THERAPY. NUCLEIC ACIDS 2018; 13:568-579. [PMID: 30439645 PMCID: PMC6234518 DOI: 10.1016/j.omtn.2018.10.005] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 07/30/2018] [Accepted: 10/05/2018] [Indexed: 01/23/2023]
Abstract
Since the first microRNA (miRNA) was discovered, a lot of studies have confirmed the associations between miRNAs and human complex diseases. Besides, obtaining and taking advantage of association information between miRNAs and diseases play an increasingly important role in improving the treatment level for complex diseases. However, due to the high cost of traditional experimental methods, many researchers have proposed different computational methods to predict potential associations between miRNAs and diseases. In this work, we developed a computational model of Random Forest for miRNA-disease association (RFMDA) prediction based on machine learning. The training sample set for RFMDA was constructed according to the human microRNA disease database (HMDD) version (v.)2.0, and the feature vectors to represent miRNA-disease samples were defined by integrating miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. The Random Forest algorithm was first employed to infer miRNA-disease associations. In addition, a filter-based method was implemented to select robust features from the miRNA-disease feature set, which could efficiently distinguish related miRNA-disease pairs from unrelated miRNA-disease pairs. RFMDA achieved areas under the curve (AUCs) of 0.8891, 0.8323, and 0.8818 ± 0.0014 under global leave-one-out cross-validation, local leave-one-out cross-validation, and 5-fold cross-validation, respectively, which were higher than many previous computational models. To further evaluate the accuracy of RFMDA, we carried out three types of case studies for four human complex diseases. As a result, 43 (esophageal neoplasms), 46 (lymphoma), 47 (lung neoplasms), and 48 (breast neoplasms) of the top 50 predicted disease-related miRNAs were verified by experiments in different kinds of case studies. The results of cross-validation and case studies indicated that RFMDA is a reliable model for predicting miRNA-disease associations.
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Affiliation(s)
- Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
| | - Chun-Chun Wang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi 830011, China.
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99
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Huang Z, Li N, Shan Y, Liang C. Hsa-miRNA-29a protects against high glucose-induced damage in human umbilical vein endothelial cells. J Cell Biochem 2018; 120:5860-5868. [PMID: 30520097 DOI: 10.1002/jcb.27871] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Accepted: 09/19/2018] [Indexed: 12/30/2022]
Abstract
Sustained exposure to high glucose (HG) results in dysfunction of vascular endothelial cells. Hence, diabetic patients often suffer from secondary vascular damages, such as vascular sclerosis and thrombogenesis, which may eventually cause cardiovascular problems. Thus, elucidating how HG results in vascular endothelial cell damage and finding an approach for prevention are important to prevent and treat vascular damages in diabetic patients. In the current study, we first showed that 72-hour exposure to HG-decreased hsa-miRNA-29a and increased the expression of Bcl-2 associated X protein (Bax), which subsequently inhibited Bcl-2 and promoted the expression of apoptotic protease activating factor-1 and activation of caspase-3, thus directly triggering the mitochondrial apoptotic pathway in human umbilical vein endothelial cells (HUVECs). Study of the underlying mechanism showed that hsa-miRNA-29a/Bax plays an essential role in the decreased proliferation and increased apoptosis of HUVECs induced by HG, and overexpression of hsa-miRNA-29a effectively inhibits HG-induced apoptosis and restores the proliferation and tube formation of HUVECs exposed to HG by inhibiting its target gene Bax. In short, our study demonstrates that hsa-miRNA-29a is a promising target for the prevention and treatment of vascular injury in diabetic patients.
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Affiliation(s)
- Zhigang Huang
- Department of Cardiology, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Na Li
- Department of Cardiology, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Yi Shan
- Department of Emergency and ICU, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Chun Liang
- Department of Cardiology, Changzheng Hospital, Second Military Medical University, Shanghai, China
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100
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Yu SP, Liang C, Xiao Q, Li GH, Ding PJ, Luo JW. MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation. J Cell Mol Med 2018; 23:1427-1438. [PMID: 30499204 PMCID: PMC6349206 DOI: 10.1111/jcmm.14048] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 11/02/2018] [Indexed: 12/20/2022] Open
Abstract
MiRNAs are a class of small non‐coding RNAs that are involved in the development and progression of various complex diseases. Great efforts have been made to discover potential associations between miRNAs and diseases recently. As experimental methods are in general expensive and time‐consuming, a large number of computational models have been developed to effectively predict reliable disease‐related miRNAs. However, the inherent noise and incompleteness in the existing biological datasets have inevitably limited the prediction accuracy of current computational models. To solve this issue, in this paper, we propose a novel method for miRNA‐disease association prediction based on matrix completion and label propagation. Specifically, our method first reconstructs a new miRNA/disease similarity matrix by matrix completion algorithm based on known experimentally verified miRNA‐disease associations and then utilizes the label propagation algorithm to reliably predict disease‐related miRNAs. As a result, MCLPMDA achieved comparable performance under different evaluation metrics and was capable of discovering greater number of true miRNA‐disease associations. Moreover, case study conducted on Breast Neoplasms further confirmed the prediction reliability of the proposed method. Taken together, the experimental results clearly demonstrated that MCLPMDA can serve as an effective and reliable tool for miRNA‐disease association prediction.
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Affiliation(s)
- Sheng-Peng Yu
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Cheng Liang
- School of Information Science and Engineering, Shandong Normal University, Jinan, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Guang-Hui Li
- School of Information Engineering, East China Jiaotong University, Nanchang, China
| | - Ping-Jian Ding
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Jia-Wei Luo
- College of Information Science and Engineering, Hunan University, Changsha, China
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