Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelizable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers.

Beretta, S., Castelli, M., Martinez, Y., Munoz, L., Silva, S., Trujillo, L., et al. (2016). A Machine Learning Approach for the Integration of miRNA-Target Predictions. In Proceedings - 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016 (pp.528-534). Institute of Electrical and Electronics Engineers Inc. [10.1109/PDP.2016.125].

A Machine Learning Approach for the Integration of miRNA-Target Predictions

BERETTA, STEFANO
Primo
;
CASTELLI, MAURO
Secondo
;
MILANESI, LUCIANO
Penultimo
;
MERELLI, IVAN
Ultimo
2016

Abstract

Although several computational methods have been developed for predicting interactions between miRNA and target genes, there are substantial differences in the achieved results. For this reason, machine learning approaches are widely used for integrating the predictions obtained from different tools. In this work we adopt a method, called M3GP, which relies on a genetic programming approach, to classify results from three tools: miRanda, TargetScan, and RNAhybrid. Such algorithm is highly parallelizable and its adoption provides great advantages while handling problems involving big datasets, since it is independent from the implementation and from the architecture on which it is executed. More precisely, we apply this technique for the classification of the achieved miRNA target predictions and we compare its results with those obtained with other classifiers.
Si
paper
Evolutionary Algorithm; Genetic Programming; miRNA-Target Prediction; Parallel Computing; Computer Networks and Communications; Hardware and Architecture; Software; Control and Optimization
English
Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP - 17 - 19 February
9781467387750
Beretta, S., Castelli, M., Martinez, Y., Munoz, L., Silva, S., Trujillo, L., et al. (2016). A Machine Learning Approach for the Integration of miRNA-Target Predictions. In Proceedings - 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016 (pp.528-534). Institute of Electrical and Electronics Engineers Inc. [10.1109/PDP.2016.125].
Beretta, S; Castelli, M; Martinez, Y; Munoz, L; Silva, S; Trujillo, L; Milanesi, L; Merelli, I
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/135344
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