A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP

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@Article{Beretta:2018:complexity,

• author = "Stefano Beretta and Mauro Castelli and Luis Munoz and Leonardo Trujillo and Yuliana Martinez and Ales Popovic and Luciano Milanesi and Ivan Merelli",
• title = "A Scalable Genetic Programming Approach to Integrate {miRNA}-Target Predictions: Comparing Different Parallel Implementations of {M3GP}",
• journal = "Complexity",
• year = "2018",
• volume = "2018",
• pages = "Article ID 4963139",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://downloads.hindawi.com/journals/complexity/2018/4963139.pdf",
• DOI = "doi:10.1155/2018/4963139",
• size = "13 pages",
• 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 parallelisable, it can be used to achieve genome-wide predictions, and its adoption provides great advantages when handling big datasets.",

}

Genetic Programming entries for Stefano Beretta Mauro Castelli Luis Munoz Delgado Leonardo Trujillo Yuliana Martinez Ales Popovic Luciano Milanesi Ivan Merelli

Citations