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

Stefano Beretta, Mauro Castelli, Yuliana Martinez, Luis Munoz, Leonardo Trujillo, Luciano Milanesi, Ivan Merelli, Sara Silva

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages528-534
Number of pages7
ISBN (Electronic)9781467387750
DOIs
Publication statusPublished - 31 Mar 2016
Event24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016 - Heraklion, Crete, Greece
Duration: 17 Feb 201619 Feb 2016

Conference

Conference24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016
CountryGreece
CityHeraklion, Crete
Period17/02/1619/02/16

Keywords

  • Evolutionary Algorithm
  • Genetic Programming
  • miRNA-Target Prediction
  • Parallel Computing

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