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Ensemble Methods for MiRNA Target Prediction from Expression Data.

Le TD, Zhang J, Liu L, Li J - PLoS ONE (2015)

Bottom Line: On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets.Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.

ABSTRACT

Background: microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.

Results: In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.

No MeSH data available.


Related in: MedlinePlus

Comparison of eight individual miRNA target prediction methods.
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pone.0131627.g001: Comparison of eight individual miRNA target prediction methods.

Mentions: In this section, we compare the performance of eight individual methods on the three datasets. For each miRNA, we extract the top 100, 200, 300, and 400 target genes ranked by each method for validation. We only keep in the validations the miRNAs that have at least one confirmed target predicted by all the methods. We then compare the performance of the methods for each miRNA based on the number of confirmed targets using defined ground truth. As we have eight methods, with respect to each miRNA, we score each method using a number (called ranking score) in the range of 1 to 8, with 8 indicating the best method and 1 the worst method. Finally, we calculate the ranking score of each method for a dataset by summing up its scores for all miRNAs. The higher the ranking score of a method, the better the method is. Fig 1 shows the comparison in terms of their ranking scores in each of the datasets (Fig 1(a), 1(b) and 1(c)), and the overall ranking score in all three datasets (Fig 1(d)). The overall ranking score is calculated by summing up the scores in all miRNAs in all three datasets.


Ensemble Methods for MiRNA Target Prediction from Expression Data.

Le TD, Zhang J, Liu L, Li J - PLoS ONE (2015)

Comparison of eight individual miRNA target prediction methods.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4482624&req=5

pone.0131627.g001: Comparison of eight individual miRNA target prediction methods.
Mentions: In this section, we compare the performance of eight individual methods on the three datasets. For each miRNA, we extract the top 100, 200, 300, and 400 target genes ranked by each method for validation. We only keep in the validations the miRNAs that have at least one confirmed target predicted by all the methods. We then compare the performance of the methods for each miRNA based on the number of confirmed targets using defined ground truth. As we have eight methods, with respect to each miRNA, we score each method using a number (called ranking score) in the range of 1 to 8, with 8 indicating the best method and 1 the worst method. Finally, we calculate the ranking score of each method for a dataset by summing up its scores for all miRNAs. The higher the ranking score of a method, the better the method is. Fig 1 shows the comparison in terms of their ranking scores in each of the datasets (Fig 1(a), 1(b) and 1(c)), and the overall ranking score in all three datasets (Fig 1(d)). The overall ranking score is calculated by summing up the scores in all miRNAs in all three datasets.

Bottom Line: On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets.Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.

ABSTRACT

Background: microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory.

Results: In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth for validation are available in the Supplementary materials.

No MeSH data available.


Related in: MedlinePlus