LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

Zhang, Yi; Chen, Min; Cheng, Xiaohui; Chen, Zheng

doi:10.1039/c9ra05554a

Cited by 7 publications

(3 citation statements)

References 90 publications

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“…Considering that global network similarity can improve prediction accuracy more effectively than local network similarity, Chen et al 12 proposed a method called NetCBI, which uses network consistency to predict associations between miRNAs and diseases. Chen et al also proposed a series of miRNA–disease association methods 13 , 14 , 15 by calculating graph Laplacian scores to obtain network consistency similarity. In 2012, Chen et al 16 proposed a random walk‐based association prediction model called RWRMDA, which is simple to implement but cannot predict isolated diseases or new miRNAs without any known associations.…”

Section: Introductionmentioning

confidence: 99%

SCPLPA: An miRNA–disease association prediction model based on spatial consistency projection and label propagation algorithm

Chen,

Deng,

et al. 2024

J Cellular Molecular Medi

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Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA–disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA–disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

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Section: Introductionmentioning

confidence: 99%

SCPLPA: An miRNA–disease association prediction model based on spatial consistency projection and label propagation algorithm

Chen,

Deng,

et al. 2024

J Cellular Molecular Medi

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“…Chen et al [ 21 ] predicted miRNA–disease associations by using Jaccard similarity and hubness-aware regression on a bipartite graph; Chen et al [ 22 ] predicted miRNA–disease associations by using common neighbor information from a bipartite graph. Chen et al [ 23 ] and Zhang et al [ 24 , 25 ] predicted miRNA–disease associations by using network projection on a bipartite graph. Chen et al [ 26 ] and Li et al [ 27 ] predicted miRNA–disease associations by using label propagation algorithm in heterogeneous networks.…”

Section: Introductionmentioning

confidence: 99%

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

Deng

Yan

et al. 2021

PLoS ONE

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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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“…utilized recommendation systems to predict the associations between environmental factors, miRNAs, and diseases, but these cannot predict isolated diseases (without any known associated miRNAs) and new miRNAs (without any known associated diseases). Zhang Y. et al (2019) used bipartite network projection (LSGSP) with known associations to reconstruct the family information, miRNA similarity network, and disease similarity network for predicting the potential miRNA-disease associations. Although LSGSP does not need negative samples, it cannot achieve good performance only with limited number of known associations.…”

Section: Introductionmentioning

confidence: 99%

MSFSP: A Novel miRNA–Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

et al. 2020

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Growing evidences have indicated that microRNAs (miRNAs) play a significant role relating to many important bioprocesses; their mutations and disorders will cause the occurrence of various complex diseases. The prediction of miRNAs associated with underlying diseases via computational approaches is beneficial to identify biomarkers and discover specific medicine, which can greatly reduce the cost of diagnosis, cure, prognosis, and prevention of human diseases. However, how to further achieve a more reliable prediction of potential miRNA-disease associations with effective integration of different biological data is a challenge for researchers. In this study, we proposed a computational model by using a federated method of combined multiple-similarities fusion and space projection (MSFSP). MSFSP firstly fused the integrated disease similarity (composed of disease semantic similarity, disease functional similarity, and disease Hamming similarity) with the integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity, and miRNA Hamming similarity). Secondly, it constructed the weighted network of miRNA-disease associations from the experimentally verified Boolean network of miRNA-disease associations by using similarity networks. Finally, it calculated the prediction results by weighting miRNA space projection scores and the disease space projection scores. Leave-one-out cross-validation demonstrated that MSFSP has the distinguished predictive accuracy with area under the receiver operating characteristics curve (AUC) of 0.9613 better than that of five other existing models. In case studies, the predictive ability of MSFSP was further confirmed as 96 and 98% of the top 50 predictions for prostatic neoplasms and lung neoplasms were successfully validated by experimental evidences and supporting experimental evidences were also found for 100% of the top 50 predictions for isolated diseases.

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LSGSP: a novel miRNA–disease association prediction model using a Laplacian score of the graphs and space projection federated method

Abstract: Lots of research findings have indicated that the mutations and disorders of miRNAs (microRNAs) are closely related to diseases. Therefore, determining the associations between human diseases and miRNAs is key to understand the pathogenic mechanisms.

Cited by 7 publications

References 90 publications

SCPLPA: An miRNA–disease association prediction model based on spatial consistency projection and label propagation algorithm

SCPLPA: An miRNA–disease association prediction model based on spatial consistency projection and label propagation algorithm

A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method

MSFSP: A Novel miRNA–Disease Association Prediction Model by Federating Multiple-Similarities Fusion and Space Projection

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