There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelizable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.

Beretta, S., Castelli, M., Muñoz, L., Trujillo, L., Martínez, Y., Popovič, A., et al. (2018). A scalable genetic programming approach to integrate miRNA-target predictions: Comparing different parallel implementations of M3GP. COMPLEXITY, 2018, 1-13 [10.1155/2018/4963139].

A scalable genetic programming approach to integrate miRNA-target predictions: Comparing different parallel implementations of M3GP

Beretta, Stefano;Castelli, Mauro
;
Milanesi, Luciano;Merelli, Ivan
2018

Abstract

There are many molecular biology approaches to the analysis of microRNA (miRNA) and target interactions, but the experiments are complex and expensive. For this reason, in silico computational approaches able to model these molecular interactions are highly desirable. Although several computational methods have been developed for predicting the interactions between miRNA and target genes, there are substantial differences in the results achieved since most algorithms provide a large number of false positives. Accordingly, machine learning approaches are widely used to integrate predictions obtained from different tools. In this work, we adopt a method called multidimensional multiclass GP with multidimensional populations (M3GP), which relies on a genetic programming approach, to integrate and classify results from different miRNA-target prediction tools. The results are compared with those obtained with other classifiers, showing competitive accuracy. Since we aim to provide genome-wide predictions with M3GP and, considering the high number of miRNA-target interactions to test (also in different species), a parallel implementation of this algorithm is recommended. In this paper, we discuss the theoretical aspects of this algorithm and propose three different parallel implementations. We show that M3GP is highly parallelizable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.
Articolo in rivista - Articolo scientifico
Genetic Programming; Parallel Implementations; Machine Learning
English
2018
2018
1
13
4963139
open
Beretta, S., Castelli, M., Muñoz, L., Trujillo, L., Martínez, Y., Popovič, A., et al. (2018). A scalable genetic programming approach to integrate miRNA-target predictions: Comparing different parallel implementations of M3GP. COMPLEXITY, 2018, 1-13 [10.1155/2018/4963139].
File in questo prodotto:
File Dimensione Formato  
2018-Complexity-M3GP.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 2.55 MB
Formato Adobe PDF
2.55 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/204863
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
Social impact