Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA–Disease Association

Chen, Min; Liao, Bo; Li, Zejun

doi:10.1038/s41598-018-24532-7

Cited by 35 publications

(27 citation statements)

References 85 publications

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“…However, the prediction results may be affected by the quality of the dataset as well as those lncRNAs with low expression level. Numerous researchers introduced random walk into the prediction of lncRNA-disease associations [44][45][46][47][48][49][50][51][52][53][54]. Sun et al [55] executed random walk with restart (RWR) on lncRNA functional similarity network to infer lncRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

Zhang

Chen

et al. 2020

IJMS

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Long non-coding RNAs (long ncRNAs, lncRNAs) of all kinds have been implicated in a range of cell developmental processes and diseases, while they are not translated into proteins. Inferring diseases associated lncRNAs by computational methods can be helpful to understand the pathogenesis of diseases, but those current computational methods still have not achieved remarkable predictive performance: such as the inaccurate construction of similarity networks and inadequate numbers of known lncRNA–disease associations. In this research, we proposed a lncRNA–disease associations inference based on integrated space projection scores (LDAI-ISPS) composed of the following key steps: changing the Boolean network of known lncRNA–disease associations into the weighted networks via combining all the global information (e.g., disease semantic similarities, lncRNA functional similarities, and known lncRNA–disease associations); obtaining the space projection scores via vector projections of the weighted networks to form the final prediction scores without biases. The leave-one-out cross validation (LOOCV) results showed that, compared with other methods, LDAI-ISPS had a higher accuracy with area-under-the-curve (AUC) value of 0.9154 for inferring diseases, with AUC value of 0.8865 for inferring new lncRNAs (whose associations related to diseases are unknown), with AUC value of 0.7518 for inferring isolated diseases (whose associations related to lncRNAs are unknown). A case study also confirmed the predictive performance of LDAI-ISPS as a helper for traditional biological experiments in inferring the potential LncRNA–disease associations and isolated diseases.

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Section: Introductionmentioning

confidence: 99%

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

Zhang

Chen

et al. 2020

IJMS

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“…The collaborative filtering algorithm was further improved by incorporating similarity matrices to enable the prediction of a new miRNA and a particular disease without known associations. Chen et al (2018) proposed a Two-tier Random Walk method in which they designed a Laplacian score of graphs for the prediction of disease-related miRNAs (GSTRW). This method can predict the correlation of all diseases with miRNAs simultaneously without negative samples.…”

Section: Introductionmentioning

confidence: 99%

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association

Wang

Gao

et al. 2020

Front. Genet.

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Accumulating biological and clinical evidence has confirmed the important associations between microRNAs (miRNAs) and a variety of human diseases. Predicting disease-related miRNAs is beneficial for understanding the molecular mechanisms of pathological conditions at the miRNA level, and facilitating the finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. However, the challenge for researchers is to establish methods that can effectively combine different datasets and make reliable predictions. In this work, we propose the method of Multi-Similarity based Combinative Hypergraph Learning for Predicting MiRNA-disease Association (MSCHLMDA). To establish this method, complex features were extracted by two measures for each miRNA-disease pair. Then, K-nearest neighbor (KNN) and K-means algorithm were used to construct two different hypergraphs. Finally, results from combinative hypergraph learning were used for predicting miRNA-disease association. In order to evaluate the prediction performance of our method, leave-one-out cross validation and 5-fold cross validation was implemented, showing that our method had significantly improved prediction performance compared to previously used methods. Moreover, three case studies on different human complex diseases were performed, which further demonstrated the predictive performance of MSCHLMDA. It is anticipated that MSCHLMDA would become an excellent complement to the biomedical research field in the future.

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“…Bipartite heterogeneous network method based on co-neighbor (Chen et al, 2019a), ELLPMDA of ensemble learning and link prediction (Chen et al, 2018j), and label propagation model with linear neighborhood (Li et al, 2018) were used for various types of miRNA-disease association prediction, but those did not figure out the easy way for parameter optimization. Random walk on heterogeneous network (Chen et al, 2012(Chen et al, , 2016a(Chen et al, , 2018aXuan et al, 2015;Liu et al, 2017;Luo and Xiao, 2017;Mugunga et al, 2017;Peng et al, 2018) used for inferring miRNA-disease associations has achieved excellent prediction results with global attributes, but all of their results were partial to such miRNAs that have more known associations with diseases.…”

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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Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA–Disease Association

Cited by 35 publications

References 85 publications

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

LDAI-ISPS: LncRNA–Disease Associations Inference Based on Integrated Space Projection Scores

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association

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

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