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RBMMMDA: predicting multiple types of disease-microRNA associations.

Chen X, Clarence Yan C, Zhang X, Li Z, Deng L, Zhang Y, Dai Q - Sci Rep (2015)

Bottom Line: Accumulating evidences have shown that plenty of miRNAs play fundamental and important roles in various biological processes and the deregulations of miRNAs are associated with a broad range of human diseases.To our knowledge, RBMMMDA is the first model which could computationally infer association types of miRNA-disease pairs.Leave-one-out cross validation was implemented for RBMMMDA and the AUC of 0.8606 demonstrated the reliable and effective performance of RBMMMDA.

View Article: PubMed Central - PubMed

Affiliation: National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China.

ABSTRACT
Accumulating evidences have shown that plenty of miRNAs play fundamental and important roles in various biological processes and the deregulations of miRNAs are associated with a broad range of human diseases. However, the mechanisms underlying the dysregulations of miRNAs still have not been fully understood yet. All the previous computational approaches can only predict binary associations between diseases and miRNAs. Predicting multiple types of disease-miRNA associations can further broaden our understanding about the molecular basis of diseases in the level of miRNAs. In this study, the model of Restricted Boltzmann machine for multiple types of miRNA-disease association prediction (RBMMMDA) was developed to predict four different types of miRNA-disease associations. Based on this model, we could obtain not only new miRNA-disease associations, but also corresponding association types. To our knowledge, RBMMMDA is the first model which could computationally infer association types of miRNA-disease pairs. Leave-one-out cross validation was implemented for RBMMMDA and the AUC of 0.8606 demonstrated the reliable and effective performance of RBMMMDA. In the case studies about lung cancer, breast cancer, and global prediction for all the diseases simultaneously, 50, 42, and 45 out of top 100 predicted miRNA-disease association types were confirmed by recent biological experimental literatures, respectively.

No MeSH data available.


Related in: MedlinePlus

Performance evaluation of RBMMMDA in term of ROC curve and AUC based on LOOCV.As a result, RBMMMDA achieved a reliable AUC of 0.8606, demonstrating the reliable predictive ability of RBMMMDA. More importantly, RBMMMDA is the first method which could computationally predict the multiple types of miRNA-disease associations.
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f1: Performance evaluation of RBMMMDA in term of ROC curve and AUC based on LOOCV.As a result, RBMMMDA achieved a reliable AUC of 0.8606, demonstrating the reliable predictive ability of RBMMMDA. More importantly, RBMMMDA is the first method which could computationally predict the multiple types of miRNA-disease associations.

Mentions: Finally, Receiver-Operating Characteristics (ROC) curve which plots true positive rate (TPR, sensitivity) versus false positive rate (FPR, 1-specificity) was drawn. Sensitivity refers to the percentage of the test miRNA-disease associations which are ranked higher than the given threshold. And specificity refers to the percentage of miRNA-disease associations that are below the threshold. Then the area under ROC curve (AUC) was calculated to evaluate the performance of RBMMMDA method. If AUC = 1, it means that the RBMMMDA method has perfect performance. And AUC = 0.5 indicates random performance. As a result, RBMMMDA achieved a reliable AUC of 0.8606 (See Fig. 1). Considering RBMMMDA is the first method to predict the multiple types of miRNA-disease associations, therefore there is no other method to implement performance comparisons. However, excellent predictive ability of RBMMMDA has been demonstrated based on the above LOOCV.


RBMMMDA: predicting multiple types of disease-microRNA associations.

Chen X, Clarence Yan C, Zhang X, Li Z, Deng L, Zhang Y, Dai Q - Sci Rep (2015)

Performance evaluation of RBMMMDA in term of ROC curve and AUC based on LOOCV.As a result, RBMMMDA achieved a reliable AUC of 0.8606, demonstrating the reliable predictive ability of RBMMMDA. More importantly, RBMMMDA is the first method which could computationally predict the multiple types of miRNA-disease associations.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4561957&req=5

f1: Performance evaluation of RBMMMDA in term of ROC curve and AUC based on LOOCV.As a result, RBMMMDA achieved a reliable AUC of 0.8606, demonstrating the reliable predictive ability of RBMMMDA. More importantly, RBMMMDA is the first method which could computationally predict the multiple types of miRNA-disease associations.
Mentions: Finally, Receiver-Operating Characteristics (ROC) curve which plots true positive rate (TPR, sensitivity) versus false positive rate (FPR, 1-specificity) was drawn. Sensitivity refers to the percentage of the test miRNA-disease associations which are ranked higher than the given threshold. And specificity refers to the percentage of miRNA-disease associations that are below the threshold. Then the area under ROC curve (AUC) was calculated to evaluate the performance of RBMMMDA method. If AUC = 1, it means that the RBMMMDA method has perfect performance. And AUC = 0.5 indicates random performance. As a result, RBMMMDA achieved a reliable AUC of 0.8606 (See Fig. 1). Considering RBMMMDA is the first method to predict the multiple types of miRNA-disease associations, therefore there is no other method to implement performance comparisons. However, excellent predictive ability of RBMMMDA has been demonstrated based on the above LOOCV.

Bottom Line: Accumulating evidences have shown that plenty of miRNAs play fundamental and important roles in various biological processes and the deregulations of miRNAs are associated with a broad range of human diseases.To our knowledge, RBMMMDA is the first model which could computationally infer association types of miRNA-disease pairs.Leave-one-out cross validation was implemented for RBMMMDA and the AUC of 0.8606 demonstrated the reliable and effective performance of RBMMMDA.

View Article: PubMed Central - PubMed

Affiliation: National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China.

ABSTRACT
Accumulating evidences have shown that plenty of miRNAs play fundamental and important roles in various biological processes and the deregulations of miRNAs are associated with a broad range of human diseases. However, the mechanisms underlying the dysregulations of miRNAs still have not been fully understood yet. All the previous computational approaches can only predict binary associations between diseases and miRNAs. Predicting multiple types of disease-miRNA associations can further broaden our understanding about the molecular basis of diseases in the level of miRNAs. In this study, the model of Restricted Boltzmann machine for multiple types of miRNA-disease association prediction (RBMMMDA) was developed to predict four different types of miRNA-disease associations. Based on this model, we could obtain not only new miRNA-disease associations, but also corresponding association types. To our knowledge, RBMMMDA is the first model which could computationally infer association types of miRNA-disease pairs. Leave-one-out cross validation was implemented for RBMMMDA and the AUC of 0.8606 demonstrated the reliable and effective performance of RBMMMDA. In the case studies about lung cancer, breast cancer, and global prediction for all the diseases simultaneously, 50, 42, and 45 out of top 100 predicted miRNA-disease association types were confirmed by recent biological experimental literatures, respectively.

No MeSH data available.


Related in: MedlinePlus