RFMirTarget: Predicting Human MicroRNA Target Genes with a Random Forest Classifier
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RFMirTarget: Predicting Human MicroRNA Target Genes with a Random Forest Classifier
MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in term...
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MicroRNAs (miRNAs) are key regulators of eukaryotic gene expression whose fundamental role has been already identified in many cell pathways. The correct identification of miRNAs targets is a major challenge in bioinformatics. So far, machine learning-based methods for miRNA-target prediction have shown the best results in terms of specificity and sensitivity. However, despite its well-known ef...
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The original Random Forest derives the final result with respect to the number of leaf nodes voted for the corresponding class. Each leaf node is treated equally and the class with the most number of votes wins. Certain leaf nodes in the topology have better classification accuracies and others often lead to a wrong decision. Also the performance of the forest for different classes differs due ...
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MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nucleotides in length, which play important roles in regulating gene expression post-transcriptionally. Several computational methods and algorithms have been developed to predict miRNA targets. In this study, we described a method that can be used to integrate miRNA target prediction data from multiple sources and gene expression...
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ژورنال
عنوان ژورنال: PLoS ONE
سال: 2013
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0070153