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Lu P, Jiang J. AE-RW: Predicting miRNA-disease associations by using autoencoder and random walk on miRNA-gene-disease heterogeneous network. Comput Biol Chem 2024; 110:108085. [PMID: 38754260 DOI: 10.1016/j.compbiolchem.2024.108085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/04/2024] [Accepted: 04/23/2024] [Indexed: 05/18/2024]
Abstract
Since scientific investigations have demonstrated that aberrant expression of miRNAs brings about the incidence of numerous intricate diseases, precise determination of miRNA-disease relationships greatly contributes to the advancement of human medical progress. To tackle the issue of inefficient conventional experimental approaches, numerous computational methods have been proposed to predict miRNA-disease association with enhanced accuracy. However, constructing miRNA-gene-disease heterogeneous network by incorporating gene information has been relatively under-explored in existing computational techniques. Accordingly, this paper puts forward a technique to predict miRNA-disease association by applying autoencoder and implementing random walk on miRNA-gene-disease heterogeneous network(AE-RW). Firstly, we integrate association information and similarities between miRNAs, genes, and diseases to construct a miRNA-gene-disease heterogeneous network. Subsequently, we consolidate two network feature representations extracted independently via an autoencoder and a random walk procedure. Finally, deep neural network(DNN) are utilized to conduct association prediction. The experimental results demonstrate that the AE-RW model achieved an AUC of 0.9478 through 5-fold CV on the HMDD v3.2 dataset, outperforming the five most advanced existing models. Additionally, case studies were implemented for breast and lung cancer, further validated the superior predictive capabilities of our model.
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Affiliation(s)
- Pengli Lu
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
| | - Jicheng Jiang
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, Gansu, PR China.
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Guo R, Chen H, Wang W, Wu G, Lv F. Predicting potential miRNA-disease associations based on more reliable negative sample selection. BMC Bioinformatics 2022; 23:432. [PMID: 36253735 PMCID: PMC9575264 DOI: 10.1186/s12859-022-04978-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 10/06/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Increasing biomedical studies have shown that the dysfunction of miRNAs is closely related with many human diseases. Identifying disease-associated miRNAs would contribute to the understanding of pathological mechanisms of diseases. Supervised learning-based computational methods have continuously been developed for miRNA-disease association predictions. Negative samples of experimentally-validated uncorrelated miRNA-disease pairs are required for these approaches, while they are not available due to lack of biomedical research interest. Existing methods mainly choose negative samples from the unlabelled ones randomly. Therefore, the selection of more reliable negative samples is of great importance for these methods to achieve satisfactory prediction results. RESULTS In this study, we propose a computational method termed as KR-NSSM which integrates two semi-supervised algorithms to select more reliable negative samples for miRNA-disease association predictions. Our method uses a refined K-means algorithm for preliminary screening of likely negative and positive miRNA-disease samples. A Rocchio classification-based method is applied for further screening to receive more reliable negative and positive samples. We implement ablation tests in KR-NSSM and find that the combination of the two selection procedures would obtain more reliable negative samples for miRNA-disease association predictions. Comprehensive experiments based on fivefold cross-validations demonstrate improvements in prediction accuracy on six classic classifiers and five known miRNA-disease association prediction models when using negative samples chose by our method than by previous negative sample selection strategies. Moreover, 469 out of 1123 selected positive miRNA-disease associations by our method are confirmed by existing databases. CONCLUSIONS Our experiments show that KR-NSSM can screen out more reliable negative samples from the unlabelled ones, which greatly improves the performance of supervised machine learning methods in miRNA-disease association predictions. We expect that KR-NSSM would be a useful tool in negative sample selection in biomedical research.
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Affiliation(s)
- Ruiyu Guo
- School of Software, East China Jiaotong University, Nanchang, 330013, China
| | - Hailin Chen
- School of Software, East China Jiaotong University, Nanchang, 330013, China.
| | - Wengang Wang
- School of Software, East China Jiaotong University, Nanchang, 330013, China
| | - Guangsheng Wu
- School of Mathematics and Computer Science, Xinyu University, Xinyu, 338004, China
| | - Fangliang Lv
- School of Software, East China Jiaotong University, Nanchang, 330013, China
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Yu L, Zheng Y, Ju B, Ao C, Gao L. Research progress of miRNA-disease association prediction and comparison of related algorithms. Brief Bioinform 2022; 23:6542222. [PMID: 35246678 DOI: 10.1093/bib/bbac066] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/30/2022] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA-disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA-disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D5430 and D6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA-disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Yujia Zheng
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Bingyi Ju
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Chunyan Ao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, China
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Wu Y, Zhu D, Wang X, Zhang S. An ensemble learning framework for potential miRNA-disease association prediction with positive-unlabeled data. Comput Biol Chem 2021; 95:107566. [PMID: 34534906 DOI: 10.1016/j.compbiolchem.2021.107566] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 08/13/2021] [Accepted: 08/18/2021] [Indexed: 11/17/2022]
Abstract
To explore the pathogenic mechanisms of MicroRNA (miRNA) on diverse diseases, many researchers have concentrated on discovering the potential associations between miRNA and disease using machine learning methods. However, the prediction accuracy of supervised machine learning methods is limited by lacking of experimentally-validated uncorrelated miRNA-disease pairs. Without these negative samples, training a highly accurate model is much more difficult. Different from traditional miRNA-disease prediction models using randomly selected unknown samples as negative training samples, we propose an ensemble learning framework to solve this positive-unlabeled (PU) learning problem. The framework incorporates two steps, i.e., a novel semi-supervised Kmeans (SS-Kmeans) to extract reliable negative samples from unknown miRNA-disease pairs and subagging method to generate diverse training sample sets to make full use of those reliable negative samples for ensemble learning. Combined with effective random vector functional link (RVFL) network as prediction model, the proposed framework showed superior prediction accuracy comparing with other popular approaches. A case study on lung and gastric neoplasms further confirms the framework's efficacy at identifying miRNA disease associations.
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Affiliation(s)
- Yao Wu
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
| | - Donghua Zhu
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
| | - Xuefeng Wang
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China.
| | - Shuo Zhang
- School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
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Chu Y, Wang X, Dai Q, Wang Y, Wang Q, Peng S, Wei X, Qiu J, Salahub DR, Xiong Y, Wei DQ. MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph. Brief Bioinform 2021; 22:6261915. [PMID: 34009265 DOI: 10.1093/bib/bbab165] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.
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Affiliation(s)
- Yanyi Chu
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Xuhong Wang
- School of Electronic, Information and Electrical Engineering (SEIEE), Shanghai Jiao Tong University, China
| | - Qiuying Dai
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Yanjing Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Qiankun Wang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, China
| | | | | | - Dennis Russell Salahub
- Department of Chemistry, University of Calgary, Fellow Royal Society of Canada and Fellow of the American Association for the Advancement of Science, China
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad-Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
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Computational biology and chemistry Special section editorial: Computational analyses for miRNA. Comput Biol Chem 2021; 91:107448. [PMID: 33579616 DOI: 10.1016/j.compbiolchem.2021.107448] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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