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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

Flowchart of RBMMMDA, demonstrating the basic ideas of predicting multiple types of disease-miRNA association in the framework of RBM, which includes the basic there steps: constructing RBMs from a disease-miRNA interaction network; training RBM by CD algorithm; implementing prediction by computing conditional probabilities.
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f2: Flowchart of RBMMMDA, demonstrating the basic ideas of predicting multiple types of disease-miRNA association in the framework of RBM, which includes the basic there steps: constructing RBMs from a disease-miRNA interaction network; training RBM by CD algorithm; implementing prediction by computing conditional probabilities.

Mentions: In this study, we developed the model of Restricted Boltzmann machine for multiple types of miRNA-disease association prediction (RBMMMDA) to predict different types of miRNA-disease associations (See Fig. 2, motivated by literature by Wang and Zeng58). Based on this model, we could obtain not only new miRNA-disease associations, but also their corresponding association types. RBM has been successfully applied to many important research fields58118119.


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)

Flowchart of RBMMMDA, demonstrating the basic ideas of predicting multiple types of disease-miRNA association in the framework of RBM, which includes the basic there steps: constructing RBMs from a disease-miRNA interaction network; training RBM by CD algorithm; implementing prediction by computing conditional probabilities.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Flowchart of RBMMMDA, demonstrating the basic ideas of predicting multiple types of disease-miRNA association in the framework of RBM, which includes the basic there steps: constructing RBMs from a disease-miRNA interaction network; training RBM by CD algorithm; implementing prediction by computing conditional probabilities.
Mentions: In this study, we developed the model of Restricted Boltzmann machine for multiple types of miRNA-disease association prediction (RBMMMDA) to predict different types of miRNA-disease associations (See Fig. 2, motivated by literature by Wang and Zeng58). Based on this model, we could obtain not only new miRNA-disease associations, but also their corresponding association types. RBM has been successfully applied to many important research fields58118119.

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