A Scalable Genetic Programming Approach to Integrate miRNA-Target Predictions: Comparing Different Parallel Implementations of M3GP
Created by W.Langdon from
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- @Article{Beretta:2018:complexity,
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author = "Stefano Beretta and Mauro Castelli and Luis Munoz and
Leonardo Trujillo and Yuliana Martinez and
Ales Popovic and Luciano Milanesi and Ivan Merelli",
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title = "A Scalable Genetic Programming Approach to Integrate
{miRNA}-Target Predictions: Comparing Different
Parallel Implementations of {M3GP}",
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journal = "Complexity",
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year = "2018",
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volume = "2018",
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pages = "Article ID 4963139",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://downloads.hindawi.com/journals/complexity/2018/4963139.pdf",
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DOI = "doi:10.1155/2018/4963139",
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size = "13 pages",
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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