Computational prediction of human disease-related microRNAs by path-based random walk

Mugunga, Israel; Ju, Ying; Liu, Xiangrong; Huang, Xiaoyang

doi:10.18632/oncotarget.17226

Cited by 15 publications

(9 citation statements)

References 52 publications

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“…Computational techniques are used to estimate potential miRNA-disease relationships, as determination of miRNAsdisease relationships by biological experimental techniques is time consuming and very expensive (Mugunga, Ju, Liu, & Huang, 2017). In recent years, many new computational techniques such as WBSMDA (X.…”

Section: Introductionmentioning

confidence: 99%

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Toprak¹,

DOĞAN²

2021

European Journal of Science and Technology

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MicroRNA (miRNA) molecules, which are effective on the initiation and progression of many different diseases, are a type of noncoding RNA with a length of about 22 nucleotides. Scientists have reported the importance of miRNAs in the prevention, diagnosis, and treatment of complex human diseases. Therefore, in the last decade, researchers have been working hard to find potential miRNAdisease associations. Many computational techniques have been developed because of the experimental techniques are time-consuming and expensive used to find new relationships between miRNAs and diseases. In this study, we suggested Kernelized Bayesian matrix factorization (KBMF) technique to predict new miRNA-disease relationships. We applied 5-fold cross validation technique and obtained an average value AUC of 0.9450. Also, we applied case studies based on breast, lung, and colon neoplasms to prove the performance of KBMF technique. The results showed that KBMF can be used as a reliable computational model to reveal possible miRNA-disease relationships.

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Section: Introductionmentioning

confidence: 99%

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Toprak¹,

DOĞAN²

2021

European Journal of Science and Technology

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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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“…Li et al [44] developed a Matrix Completion for MiRNA-Disease Association prediction model based on the known miRNA-disease associations, during the matrix completion, the known associations are well retained, potential associations are predicted by the completed values. Mugunga et al [45] presented a prediction model that is based on the theory of path features and random walk to obtain a relevancy score of miRNA-related disease. In their model, highly ranked scores are potential miRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction

et al. 2019

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Since lots of miRNA-disease associations have been verified, it is meaningful to discover more miRNAdisease associations for serving disease diagnosis and prevention of human complex diseases. However, it is not practical to identify potential associations using traditional biological experimental methods since the process is expensive and time consuming. Therefore, it is necessary to develop efficient computational methods to accomplish this task. In this work, we introduced a matrix completion model with dual Laplacian regularization (DLRMC) to infer unknown miRNA-disease associations in heterogeneous omics data. Specifically, DLRMC transformed the task of miRNAdisease association prediction into a matrix completion problem, in which the potential missing entries of the miRNA-disease association matrix were calculated, the missing association can be obtained based on the prediction scores after the completion procedure. Meanwhile, the miRNA functional similarity and the disease semantic similarity were fully exploited to serve the miRNAdisease association matrix completion by using a dual Laplacian regularization term. In the experiments, we conducted global and local Leave-One-Out Cross Validation (LOOCV) and case studies to evaluate the efficacy of DLRMC on the Human miRNA-disease associations dataset obtained from the HMDDv2.0 database. As a result, the AUCs of DLRMC is 0.9174 and 0.8289 in global LOOCV and local LOOCV, respectively, which significantly outperform a variety of previous methods. In addition, in the case studies on four significant diseases related to human health including Colon Neoplasms, Kidney neoplasms, Lymphoma and Prostate neoplasms, 90%, 92%, 92% and 94% out of the top 50 predicted miRNAs has been confirmed, respectively.

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Computational prediction of human disease-related microRNAs by path-based random walk

Cited by 15 publications

References 52 publications

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

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

Dual Laplacian regularized matrix completion for microRNA-disease associations prediction

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