Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks

BMC Genomics
Bi Zhao, Bin Xue

Abstract

Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are developed in various small datasets, which each represent a limited sample space. Thus, the prediction accuracy of these tools has not been systematically validated at a larger scale. Accordingly, comparing the prediction accuracy of these tools and determining their applicability become challenging. In addition, the accuracy of these tools, especially in large datasets, needs to be improved for broader applications. In this project, a large dataset containing more than 46,600 miRNA:mRNA interactions was assembled and split into eleven subsets based on the availability of prediction scores of four individual predictors, which are miRanda, miRDB, PITA, and TargetScan. In each of these subsets, the predictive results of four individual predictors were integrated using decision-tree based artificial neural networks to make the meta-prediction. The decision-tree is used here to sort the predictive results of four individual predictors, and artificial neural networks are applied to make meta-predictio...Continue Reading

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Citations

Jun 20, 2019·Protein and Peptide Letters·Amit Sagar, Bin Xue
Apr 12, 2020·Cancers·Shahzad Nawaz Syed, Bernhard Brüne

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Methods Mentioned

BETA
Cross-Linking Immunoprecipitation

Software Mentioned

DANN
TargetScan
miRTarDANN
ComiR
PITA
MiRanda
miRDB

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