1
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Han GS, Gao Q, Peng LZ, Tang J. Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction. Interdiscip Sci 2024; 16:176-191. [PMID: 38099958 DOI: 10.1007/s12539-023-00594-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 11/05/2023] [Accepted: 11/07/2023] [Indexed: 02/22/2024]
Abstract
Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA-disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA-disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized [Formula: see text] nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as [Formula: see text]-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA-disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross validation are comparative or better with other matrix factorization models. Finally, we select three diseases including lung tumor, bladder tumor and breast tumor for case analysis, and further extend the matrix factorization model based on deep learning. The results show that the hybrid algorithm combining matrix factorization with deep learning proposed in this paper can predict miRNAs related to different diseases with high accuracy.
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Affiliation(s)
- Guo-Sheng Han
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China.
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China.
| | - Qi Gao
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China
| | - Ling-Zhi Peng
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China
| | - Jing Tang
- Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, China
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2
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Lu C, Xie M. LDAEXC: LncRNA-Disease Associations Prediction with Deep Autoencoder and XGBoost Classifier. Interdiscip Sci 2023:10.1007/s12539-023-00573-z. [PMID: 37308797 DOI: 10.1007/s12539-023-00573-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 06/14/2023]
Abstract
Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 ± 0.0043, 0.9449 ± 0.022, 0.9375 ± 0.0331 and 0.9556 ± 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. TLDAEXC utilizes disease semantic similarity, lncRNA expression similarity, and Gaussian interaction profile kernel similarity of lncRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder to extract reduced features, and an XGBoost classifier is used to predict the lncRNA-disease associations based on the reduced features. The fivefold and tenfold cross-validation experiments on a benchmark dataset showed that LDAEXC could achieve AUC scores of 0.9676 and 0.9682, respectively, significantly higher than other state-of-the-art similar methods.
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Affiliation(s)
- Cuihong Lu
- College of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Minzhu Xie
- College of Information Science and Engineering, Hunan Normal University, Changsha, China.
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3
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Qu Q, Chen X, Ning B, Zhang X, Nie H, Zeng L, Chen H, Fu X. Prediction of miRNA-disease associations by neural network-based deep matrix factorization. Methods 2023; 212:1-9. [PMID: 36813017 DOI: 10.1016/j.ymeth.2023.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/17/2023] [Accepted: 02/10/2023] [Indexed: 02/23/2023] Open
Abstract
MicroRNA(miRNA) is a class of short non-coding RNAs with a length of about 22 nucleotides, which participates in various biological processes of cells. A number of studies have shown that miRNAs are closely related to the occurrence of cancer and various human diseases. Therefore, studying miRNA-disease associations is helpful to understand the pathogenesis of diseases as well as the prevention, diagnosis, treatment and prognosis of diseases. Traditional biological experimental methods for studying miRNA-disease associations have disadvantages such as expensive equipment, time-consuming and labor-intensive. With the rapid development of bioinformatics, more and more researchers are committed to developing effective computational methods to predict miRNA-disease associations in roder to reduce the time and money cost of experiments. In this study, we proposed a neural network-based deep matrix factorization method named NNDMF to predict miRNA-disease associations. To address the problem that traditional matrix factorization methods can only extract linear features, NNDMF used neural network to perform deep matrix factorization to extract nonlinear features, which makes up for the shortcomings of traditional matrix factorization methods. We compared NNDMF with four previous classical prediction models (IMCMDA, GRMDA, SACMDA and ICFMDA) in global LOOCV and local LOOCV, respectively. The AUCs achieved by NNDMF in two cross-validation methods were 0.9340 and 0.8763, respectively. Furthermore, we conducted case studies on three important human diseases (lymphoma, colorectal cancer and lung cancer) to validate the effectiveness of NNDMF. In conclusion, NNDMF could effectively predict the potential miRNA-disease associations.
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Affiliation(s)
- Qiang Qu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xia Chen
- School of Basic Education, Changsha Aeronautical Vocational and Technical College, Changsha, China
| | - Bin Ning
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiang Zhang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Hao Nie
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Li Zeng
- College of Life and Environmental Science, Hunan University of Art and Science, Changde, China
| | - Haowen Chen
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
| | - Xiangzheng Fu
- Research Institute of Hunan University in Chongqing, Chongqing, China.
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4
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Xie GB, Chen RB, Lin ZY, Gu GS, Yu JR, Liu ZG, Cui J, Lin LQ, Chen LC. Predicting lncRNA-disease associations based on combining selective similarity matrix fusion and bidirectional linear neighborhood label propagation. Brief Bioinform 2023; 24:6966536. [PMID: 36592062 DOI: 10.1093/bib/bbac595] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/30/2022] [Accepted: 12/04/2022] [Indexed: 01/03/2023] Open
Abstract
Recent studies have revealed that long noncoding RNAs (lncRNAs) are closely linked to several human diseases, providing new opportunities for their use in detection and therapy. Many graph propagation and similarity fusion approaches can be used for predicting potential lncRNA-disease associations. However, existing similarity fusion approaches suffer from noise and self-similarity loss in the fusion process. To address these problems, a new prediction approach, termed SSMF-BLNP, based on organically combining selective similarity matrix fusion (SSMF) and bidirectional linear neighborhood label propagation (BLNP), is proposed in this paper to predict lncRNA-disease associations. In SSMF, self-similarity networks of lncRNAs and diseases are obtained by selective preprocessing and nonlinear iterative fusion. The fusion process assigns weights to each initial similarity network and introduces a unit matrix that can reduce noise and compensate for the loss of self-similarity. In BLNP, the initial lncRNA-disease associations are employed in both lncRNA and disease directions as label information for linear neighborhood label propagation. The propagation was then performed on the self-similarity network obtained from SSMF to derive the scoring matrix for predicting the relationships between lncRNAs and diseases. Experimental results showed that SSMF-BLNP performed better than seven other state of-the-art approaches. Furthermore, a case study demonstrated up to 100% and 80% accuracy in 10 lncRNAs associated with hepatocellular carcinoma and 10 lncRNAs associated with renal cell carcinoma, respectively. The source code and datasets used in this paper are available at: https://github.com/RuiBingo/SSMF-BLNP.
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Affiliation(s)
- Guo-Bo Xie
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, China
| | - Rui-Bin Chen
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhi-Yi Lin
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, China
| | - Guo-Sheng Gu
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, China
| | - Jun-Rui Yu
- School of Computer, Guangdong University of Technology, Guangzhou, 510000, China
| | - Zhen-Guo Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Ji Cui
- Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China
| | - Lie-Qing Lin
- Center of Campus Network & Modern Educational Technology, Guangdong University of Technology, Guangzhou, 510000, China
| | - Lang-Cheng Chen
- Center of Campus Network & Modern Educational Technology, Guangdong University of Technology, Guangzhou, 510000, China
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Li L, Gao Z, Zheng CH, Qi R, Wang YT, Ni JC. Predicting miRNA-Disease Association Based on Improved Graph Regression. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3604-3613. [PMID: 34757912 DOI: 10.1109/tcbb.2021.3127017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Recently, as a growing number of associations between microRNAs (miRNAs) and diseases are discovered, researchers gradually realize that miRNAs are closely related to several complicated biological processes and human diseases. Hence, it is especially important to construct availably models to infer associations between miRNAs and diseases. In this study, we presented Improved Graph Regression for miRNA-Disease Association Prediction (IGRMDA) to observe potential relationship between miRNAs and diseases. In order to reduce the inherent noise existing in the acquired biological datasets, we utilized matrix decomposition algorithm to process miRNA functional similarity and disease semantic similarity and then combining them with existing similarity information to obtain final miRNA similarity data and disease similarity data. Then, we applied miRNA-disease association data, miRNA similarity data and disease similarity data to form corresponding latent spaces. Furthermore, we performed improved graph regression algorithm in latent spaces, which included miRNA-disease association space, miRNA similarity space and disease similarity space. Non-negative matrix factorization and partial least squares were used in the graph regression process to obtain important related attributes. The cross validation experiments and case studies were also implemented to prove the effectiveness of IGRMDA, which showed that IGRMDA could predict potential associations between miRNAs and diseases.
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Wang B, Wang X, Zheng X, Han Y, Du X. JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA. Sci Rep 2022; 12:17030. [PMID: 36220862 PMCID: PMC9552706 DOI: 10.1038/s41598-022-21243-y] [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: 05/24/2022] [Accepted: 09/26/2022] [Indexed: 12/29/2022] Open
Abstract
Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA-miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA-miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA-miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.
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Affiliation(s)
- Bo Wang
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
| | - Xinwei Wang
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
| | - Xiaodong Zheng
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
| | - Yu Han
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
| | - Xiaoxin Du
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
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7
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Shang J, Yang Y, Li F, Guan B, Liu JX, Sun Y. BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network. BMC Genomics 2022; 23:686. [PMID: 36199016 PMCID: PMC9533620 DOI: 10.1186/s12864-022-08908-8] [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: 07/09/2022] [Accepted: 09/26/2022] [Indexed: 11/12/2022] Open
Abstract
Background MicroRNAs (miRNAs) have been confirmed to be inextricably linked to the emergence of human complex diseases. The identification of the disease-related miRNAs has gradually become a routine way to unveil the genetic mechanisms of examined disorders. Methods In this study, a method BLNIMDA based on a weighted bi-level network was proposed for predicting hidden associations between miRNAs and diseases. For this purpose, the known associations between miRNAs and diseases as well as integrated similarities between miRNAs and diseases are mapped into a bi-level network. Based on the developed bi-level network, the miRNA-disease associations (MDAs) are defined as strong associations, potential associations and no associations. Then, each miRNA-disease pair (MDP) is assigned two information properties according to the bidirectional information distribution strategy, i.e., associations of miRNA towards disease and vice-versa. Finally, two affinity weights for each MDP obtained from the information properties and the association type are then averaged as the final association score of the MDP. Highlights of the BLNIMDA lie in the definition of MDA types, and the introduction of affinity weights evaluation from the bidirectional information distribution strategy and defined association types, which ensure the comprehensiveness and accuracy of the final prediction score of MDAs. Results Five-fold cross-validation and leave-one-out cross-validation are used to evaluate the performance of the BLNIMDA. The results of the Area Under Curve show that the BLNIMDA has many advantages over the other seven selected computational methods. Furthermore, the case studies based on four common diseases and miRNAs prove that the BLNIMDA has good predictive performance. Conclusions Therefore, the BLNIMDA is an effective method for predicting hidden MDAs. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-022-08908-8.
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Affiliation(s)
- Junliang Shang
- School of Computer Science, Qufu Normal University, 276826, Rizhao, China
| | - Yi Yang
- School of Computer Science, Qufu Normal University, 276826, Rizhao, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, 276826, Rizhao, China
| | - Boxin Guan
- School of Computer Science, Qufu Normal University, 276826, Rizhao, China
| | - Jin-Xing Liu
- School of Computer Science, Qufu Normal University, 276826, Rizhao, China
| | - Yan Sun
- School of Computer Science, Qufu Normal University, 276826, Rizhao, China.
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8
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Li A, Deng Y, Tan Y, Chen M. A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method. PLoS One 2021; 16:e0252971. [PMID: 34138933 PMCID: PMC8211179 DOI: 10.1371/journal.pone.0252971] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Accepted: 05/26/2021] [Indexed: 12/27/2022] Open
Abstract
A large number of studies have shown that the variation and disorder of miRNAs are important causes of diseases. The recognition of disease-related miRNAs has become an important topic in the field of biological research. However, the identification of disease-related miRNAs by biological experiments is expensive and time consuming. Thus, computational prediction models that predict disease-related miRNAs must be developed. A novel network projection-based dual random walk with restart (NPRWR) was used to predict potential disease-related miRNAs. The NPRWR model aims to estimate and accurately predict miRNA-disease associations by using dual random walk with restart and network projection technology, respectively. The leave-one-out cross validation (LOOCV) was adopted to evaluate the prediction performance of NPRWR. The results show that the area under the receiver operating characteristic curve(AUC) of NPRWR was 0.9029, which is superior to that of other advanced miRNA-disease associated prediction methods. In addition, lung and kidney neoplasms were selected to present a case study. Among the first 50 miRNAs predicted, 50 and 49 miRNAs have been proven by in databases or relevant literature. Moreover, NPRWR can be used to predict isolated diseases and new miRNAs. LOOCV and the case study achieved good prediction results. Thus, NPRWR will become an effective and accurate disease-miRNA association prediction model.
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Affiliation(s)
- Ang Li
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
| | - Yingwei Deng
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
- Hainan Key Laboratory for Computational Science and Application, Haikou, China
| | - Yan Tan
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
| | - Min Chen
- Hunan Institute of Technology, School of Computer Science and Technology, Hengyang, China
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Zhu Q, Fan Y, Pan X. Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200715165335] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with
about 22 nucleotides, and they play a significant role in a variety of complex biological processes.
Many researches have shown that miRNAs are closely related to human diseases. Although the
biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming
and costly.
Objective:
Thus, computational methods are urgently needed to effectively predict miRNA-disease
associations.
Methods:
In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk
model to predict miRNA-disease associations. Specifically, in BIRWMDA, the similarity network
fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA
similarity matrix and a disease-disease similarity matrix, then the miRNA-disease associations were
predicted by the bi-random walk model.
Results:
To evaluate the performance of BIRWMDA, we ran the leave-one-out cross-validation and
5-fold cross-validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067,
respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of
exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate
neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were
confirmed by literature, respectively. From the perspective of exploring miRNA-related diseases, we
conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted
diseases were confirmed by literatures, respectively.
Conclusion:
The fusion of multiple biological networks could effectively predict miRNA-diseases
associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease
associations.
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Affiliation(s)
- Qingqi Zhu
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Yongxian Fan
- School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China
| | - Xiaoyong Pan
- Institute of Image Processing and Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 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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11
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Liu D, Huang Y, Nie W, Zhang J, Deng L. SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost. BMC Bioinformatics 2021; 22:219. [PMID: 33910505 PMCID: PMC8082881 DOI: 10.1186/s12859-021-04135-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/14/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important. RESULTS In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively. CONCLUSION The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.
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Affiliation(s)
- Dayun Liu
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Yibiao Huang
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Wenjuan Nie
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China
| | - Jiaxuan Zhang
- Department of Cognitive Science, University of California San Diego, La Jolla, 92093, USA
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, Hunan, 410083, China.
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12
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Wang YT, Wu QW, Gao Z, Ni JC, Zheng CH. MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features. BMC Med Inform Decis Mak 2021; 21:133. [PMID: 33882934 PMCID: PMC8061020 DOI: 10.1186/s12911-020-01320-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 11/09/2020] [Indexed: 11/10/2022] Open
Abstract
Background MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time‐consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. Methods This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. Result Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. Conclusion MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA–disease association predication.
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Affiliation(s)
- Yu-Tian Wang
- School of Software, Qufu Normal University, Qufu, China
| | - Qing-Wen Wu
- School of Software, Qufu Normal University, Qufu, China
| | - Zhen Gao
- School of Software, Qufu Normal University, Qufu, China
| | - Jian-Cheng Ni
- School of Software, Qufu Normal University, Qufu, China.
| | - Chun-Hou Zheng
- School of Computer Science and Technology, Anhui University, Hefei, China. .,College of Mathematics and System Science, Xinjiang University, Urumqi, China.
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13
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Xie G, Huang B, Sun Y, Wu C, Han Y. RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation. Mol Genet Genomics 2021; 296:473-483. [PMID: 33590345 DOI: 10.1007/s00438-021-01764-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 01/28/2021] [Indexed: 12/13/2022]
Abstract
An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.
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Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Bin Huang
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computer Science, Guangdong University of Technology, Guangzhou, China.
| | - Changhai Wu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuqiong Han
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
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14
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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: 84] [Impact Index Per Article: 21.0] [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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15
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MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation. BMC Med Genomics 2019; 12:185. [PMID: 31865912 PMCID: PMC6927119 DOI: 10.1186/s12920-019-0622-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Background Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods. Results In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions. Conclusions Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.
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16
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Huang Z, Liu L, Gao Y, Shi J, Cui Q, Li J, Zhou Y. Benchmark of computational methods for predicting microRNA-disease associations. Genome Biol 2019; 20:202. [PMID: 31594544 PMCID: PMC6781296 DOI: 10.1186/s13059-019-1811-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 09/03/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND A series of miRNA-disease association prediction methods have been proposed to prioritize potential disease-associated miRNAs. Independent benchmarking of these methods is warranted to assess their effectiveness and robustness. RESULTS Based on more than 8000 novel miRNA-disease associations from the latest HMDD v3.1 database, we perform systematic comparison among 36 readily available prediction methods. Their overall performances are evaluated with rigorous precision-recall curve analysis, where 13 methods show acceptable accuracy (AUPRC > 0.200) while the top two methods achieve a promising AUPRC over 0.300, and most of these methods are also highly ranked when considering only the causal miRNA-disease associations as the positive samples. The potential of performance improvement is demonstrated by combining different predictors or adopting a more updated miRNA similarity matrix, which would result in up to 16% and 46% of AUPRC augmentations compared to the best single predictor and the predictors using the previous similarity matrix, respectively. Our analysis suggests a common issue of the available methods, which is that the prediction results are severely biased toward well-annotated diseases with many associated miRNAs known and cannot further stratify the positive samples by discriminating the causal miRNA-disease associations from the general miRNA-disease associations. CONCLUSION Our benchmarking results not only provide a reference for biomedical researchers to choose appropriate miRNA-disease association predictors for their purpose, but also suggest the future directions for the development of more robust miRNA-disease association predictors.
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Affiliation(s)
- Zhou Huang
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Leibo Liu
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China
| | - Yuanxu Gao
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Jiangcheng Shi
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
| | - Qinghua Cui
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China
- Center of Bioinformatics, Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Jianwei Li
- Institute of Computational Medicine, School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China.
| | - Yuan Zhou
- Department of Biomedical Informatics, Department of Physiology and Pathophysiology, Center for Noncoding RNA Medicine, MOE Key Lab of Cardiovascular Sciences, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing, 100191, China.
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17
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Xie G, Fan Z, Sun Y, Wu C, Ma L. WBNPMD: weighted bipartite network projection for microRNA-disease association prediction. J Transl Med 2019; 17:322. [PMID: 31547811 PMCID: PMC6757419 DOI: 10.1186/s12967-019-2063-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 09/06/2019] [Indexed: 01/21/2023] Open
Abstract
Background Recently, numerous biological experiments have indicated that microRNAs (miRNAs) play critical roles in exploring the pathogenesis of various human diseases. Since traditional experimental methods for miRNA-disease associations detection are costly and time-consuming, it becomes urgent to design efficient and robust computational techniques for identifying undiscovered interactions. Methods In this paper, we proposed a computation framework named weighted bipartite network projection for miRNA-disease association prediction (WBNPMD). In this method, transfer weights were constructed by combining the known miRNA and disease similarities, and the initial information was properly configured. Then the two-step bipartite network algorithm was implemented to infer potential miRNA-disease associations. Results The proposed WBNPMD was applied to the known miRNA-disease association data, and leave-one-out cross-validation (LOOCV) and fivefold cross-validation were implemented to evaluate the performance of WBNPMD. As a result, our method achieved the AUCs of 0.9321 and \documentclass[12pt]{minimal}
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\begin{document}$$0.9173 \pm 0.0005$$\end{document}0.9173±0.0005 in LOOCV and fivefold cross-validation, and outperformed other four state-of-the-art methods. We also carried out two kinds of case studies on prostate neoplasm, colorectal neoplasm, and lung neoplasm, and most of the top 50 predicted miRNAs were confirmed to have an association with the corresponding diseases based on dbDeMC, miR2Disease, and HMDD V3.0 databases. Conclusions The experimental results demonstrate that WBNPMD can accurately infer potential miRNA-disease associations. We anticipated that the proposed WBNPMD could serve as a powerful tool for potential miRNA-disease associations excavation. Electronic supplementary material The online version of this article (10.1186/s12967-019-2063-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Zhiliang Fan
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computer Science, Guangdong University of Technology, Guangzhou, China.
| | - Cuiming Wu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Lei Ma
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
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18
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Xie G, Meng T, Luo Y, Liu Z. SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:45-55. [PMID: 31514111 PMCID: PMC6742806 DOI: 10.1016/j.omtn.2019.07.022] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 07/13/2019] [Accepted: 07/24/2019] [Indexed: 01/24/2023]
Abstract
Recently, prediction of lncRNA-disease associations has attracted more and more attentions. Various computational models have been proposed; however, there is still room to improve the prediction accuracy. In this paper, we propose a kernel fusion method with different types of similarities for the lncRNAs and diseases. The expression similarity and cosine similarity are used for lncRNAs, and the semantic similarity and cosine similarity are used for the diseases. To eliminate the noise effect, a neighbor constraint is enforced to refine all the similarity matrices before fusion. Experimental results show that the proposed similarity kernel fusion (SKF)-LDA method has the superiority performance in terms of AUC values and other measurements. In the schemes of LOOCV and 5-fold CV, AUC values of SKF-LDA achieve 0.9049 and 0.8743±0.0050 respectively. In addition, the conducted case studies of three diseases (hepatocellular carcinoma, lung cancer, and prostate cancer) show that SKF-LDA can predict related lncRNAs accurately.
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Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Tengfei Meng
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yu Luo
- School of Computer Science, Guangdong University of Technology, Guangzhou, China.
| | - Zhenguo Liu
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
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19
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Xie G, Huang Z, Liu Z, Lin Z, Ma L. NCPHLDA: a novel method for human lncRNA–disease association prediction based on network consistency projection. Mol Omics 2019; 15:442-450. [DOI: 10.1039/c9mo00092e] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
In recent years, an increasing number of biological experiments and clinical reports have shown that lncRNA is closely related to the development of various complex human diseases.
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Affiliation(s)
- Guobo Xie
- School of Computer Science
- Guangdong University of Technology
- Guangzhou
- China
| | - Zecheng Huang
- School of Computer Science
- Guangdong University of Technology
- Guangzhou
- China
| | - Zhenguo Liu
- Department of Thoracic Surgery
- The First Affiliated Hospital of Sun Yat-sen University
- Guangzhou
- China
| | - Zhiyi Lin
- School of Computer Science
- Guangdong University of Technology
- Guangzhou
- China
| | - Lei Ma
- Institute of Automation
- Chinese Academy of Sciences
- Beijing
- China
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20
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Zhao Y, Chen X, Yin J. A Novel Computational Method for the Identification of Potential miRNA-Disease Association Based on Symmetric Non-negative Matrix Factorization and Kronecker Regularized Least Square. Front Genet 2018; 9:324. [PMID: 30186308 PMCID: PMC6111239 DOI: 10.3389/fgene.2018.00324] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 07/30/2018] [Indexed: 12/31/2022] Open
Abstract
Increasing evidence has indicated that microRNAs (miRNAs) are associated with numerous human diseases. Studying the associations between miRNAs and diseases contributes to the exploration of effective diagnostic and treatment approaches for diseases. Unfortunately, the use of biological experiments to reveal the potential associations between miRNAs and diseases is time consuming and costly. Therefore, it is very necessary to use simple and efficient calculation models to predict potential disease-related miRNAs. Considering the limitations of other previous methods, we proposed a novel computational model of Symmetric Nonnegative Matrix Factorization for MiRNA-Disease Association prediction (SNMFMDA) to reveal the relation of miRNA-disease pairs. SNMFMDA could be applied to predict miRNAs associated with new diseases. Compared to the direct use of the integrated similarity in previous computational models, the integrated similarity need to be interpolated by symmetric non-negative matrix factorization (SymNMF) before application in SNMFMDA, and the relevant probability of disease-miRNA was obtained mainly through Kronecker regularized least square (KronRLS) method in our model. What's more, the AUC of global leave-one-out cross validation (LOOCV) reached 0.9007, and the AUC based on local LOOCV was 0.8426. Besides, the mean and the standard deviation of AUCs achieved 0.8830 and 0.0017 respectively in 5-fold cross validation. All of the above results demonstrated the superior prediction performance of SNMFMDA. We also conducted three different case studies on Esophageal Neoplasms, Breast Neoplasms and Lung Neoplasms, and 49, 49, and 48 of the top 50 of their predicted miRNAs respectively were confirmed by databases or related literatures. It could be expected that SNMFMDA would be a model with the ability to predict disease-related miRNAs efficiently and accurately.
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Affiliation(s)
- Yan Zhao
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Jun Yin
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
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21
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He BS, Qu J, Zhao Q. Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization. Front Genet 2018; 9:303. [PMID: 30131824 PMCID: PMC6090164 DOI: 10.3389/fgene.2018.00303] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 07/18/2018] [Indexed: 12/12/2022] Open
Abstract
With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.
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Affiliation(s)
- Bin-Sheng He
- The First Affiliated Hospital, Changsha Medical University, Changsha, China
| | - Jia Qu
- 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
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22
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Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association. Sci Rep 2018; 8:6481. [PMID: 29691434 PMCID: PMC5915491 DOI: 10.1038/s41598-018-24532-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/03/2018] [Indexed: 12/15/2022] Open
Abstract
microRNAs (miRNAs) mutation and maladjustment are related to the occurrence and development of human diseases. Studies on disease-associated miRNA have contributed to disease diagnosis and treatment. To address the problems, such as low prediction accuracy and failure to predict the relationship between new miRNAs and diseases and so on, we design a Laplacian score of graphs to calculate the global similarity of networks and propose a Global Similarity method based on a Two-tier Random Walk for the prediction of miRNA-disease association (GSTRW) to reveal the correlation between miRNAs and diseases. This method is a global approach that can simultaneously predict the correlation between all diseases and miRNAs in the absence of negative samples. Experimental results reveal that this method is better than existing approaches in terms of overall prediction accuracy and ability to predict orphan diseases and novel miRNAs. A case study on GSTRW for breast cancer and conlon cancer is also conducted, and the majority of miRNA-disease association can be verified by our experiment. This study indicates that this method is feasible and effective.
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23
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Chen M, Peng Y, Li A, Li Z, Deng Y, Liu W, Liao B, Dai C. A novel information diffusion method based on network consistency for identifying disease related microRNAs. RSC Adv 2018; 8:36675-36690. [PMID: 35558942 PMCID: PMC9088870 DOI: 10.1039/c8ra07519k] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 10/17/2018] [Indexed: 12/27/2022] Open
Abstract
The abnormal expression of miRNAs is directly related to the development of human diseases. Predicting the potential candidate miRNAs associated with diseases can contribute to the detection, diagnosis, treatment and prevention of human complex diseases. The effective inference of the calculation method of the relationship between miRNAs and diseases is an effective supplement to biological experiments. It is of great help in the prevention, treatment and prognosis of complex diseases. This paper proposes a novel information diffusion method based on network consistency (IDNC) for identifying disease related microRNAs. The model first synthesizes the miRNA family information and the miRNA function similarity to reconstruct the miRNA network, and reconstruct the disease network by using the known disease and miRNA-related information and the semantic score between diseases. Then the global similarity of the two networks is obtained by using the Laplacian score of graphs. The global similarity score is a measure of the similarity between diseases and miRNAs. The disease–miRNA relation network was reconstructed by integrating the global similarity relation. The network consistency diffusion seed is then obtained by combining the global similarity network with the reconstructed disease–miRNA association network. Thereafter, the stable diffusion spectrum is generated as the prediction score by using the restarted random walk algorithm. The AUC value obtained by performing the LOOCV in the gold benchmark dataset is 0.8814. The AUC value obtained by performing the LOOCV in the predictive dataset is 0.9512. Compared with other frontier methods, our method has higher accuracy, which is further illustrated by case studies of breast neoplasms and colon neoplasms to prove that IDNC is valuable. The abnormal expression of miRNAs is directly related to the development of human diseases.![]()
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Affiliation(s)
- Min Chen
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
- College of Information Science and Engineering
| | - Yan Peng
- College of International Communication
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Ang Li
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Zejun Li
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
- College of Information Science and Engineering
| | - Yingwei Deng
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Wenhua Liu
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
| | - Bo Liao
- College of Information Science and Engineering
- Hunan University
- Changsha 410082
- China
| | - Chengqiu Dai
- College of Computer Science and Technology
- Hunan Institute of Technology
- 421002 Hengyang
- China
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