@article{Lezcano20,

title = {A multi-objective approach for designing optimized operation sequence on binary image processing},

author = {C. Lezcano and J.L. Vázquez Noguera and D. P. Pinto-Roa and M. García-Torres and C. Gaona and P. E. Gardel-Sotomayor},

year  = {2020},

date = {2020-01-01},

journal = {Heliyon},

volume = {6},

number = {4},

pages = {e03670},

abstract = {In binary image segmentation, the choice of the order of the operation sequence may yield to suboptimal results. In this work, we propose to tackle the associated optimization problem via multi-objective approach. Given the original image,  in combination with a list of morphological, logical and stacking operations, the goal is to obtain the ideal output at the lowest computational cost. We compared the performance of two Multi-objective Evolutionary Algorithms (MOEAs): the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). NSGA-II has better results in most cases, but the difference does not reach statistical significance. The results show that the similarity measure and the computational cost are objective functions in conflict, while the number of operations available and type of input images impact on the quality of Pareto set.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Entropy2019,

title = {Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks},

author = {F. Gómez-Vela and F. M Delgado-Chaves and D.S. Rodríguez-Baena and M. García-Torres and F. Divina},

url = {https://www.mdpi.com/1099-4300/21/12/1139},

doi = {https://doi.org/10.3390/e21121139},

year  = {2019},

date = {2019-01-01},

journal = {Entropy},

volume = {21},

number = {12},

pages = {1139},

abstract = {Gene networks have become a powerful tool in the comprehensive analysis of gene expression. Due to the increasing amount of available data, computational methods for networks generation must deal with the so-called curse of dimensionality in the quest for the reliability of the obtained results. In this context, ensemble strategies have significantly improved the precision of results by combining different measures or methods. On the other hand, structure optimization techniques are also important in the reduction of the size of the networks, not only improving their topology but also keeping a positive prediction ratio. In this work, we present Ensemble and Greedy networks (EnGNet), a novel two-step method for gene networks inference. First, EnGNet uses an ensemble strategy for co-expression networks generation. Second, a greedy algorithm optimizes both the size and the topological features of the network. Not only do achieved results show that this method is able to obtain reliable networks, but also that it significantly improves topological features. Moreover, the usefulness of the method is proven by an application to a human dataset on post-traumatic stress disorder, revealing an innate immunity-mediated response to this pathology. These results are indicative of the method’s potential in the field of biomarkers discovery and characterization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Energies2019b,

title = {A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings},

author = {F. Divina and M. García-Torres and F. Goméz-Vela and J.L. Vázquez Noguera},

url = {https://www.mdpi.com/1996-1073/12/10/1934},

doi = {https://doi.org/10.3390/en12101934},

year  = {2019},

date = {2019-01-01},

journal = {Applied Sciences},

volume = {12},

number = {10},

pages = {1934},

abstract = {Smart buildings are equipped with sensors that allow monitoring a range of building systems including heating and air conditioning, lighting and the general electric energy consumption. Thees data can then be stored and analyzed. The ability to use historical data regarding electric energy consumption could allow improving the energy efficiency of such buildings, as well as help to spot problems related to wasting of energy. This problem is even more important when considering that buildings are some of the largest consumers of energy. In this paper, we are interested in forecasting the energy consumption of smart buildings, and, to this aim, we propose a comparative study of different forecasting strategies that can be used to this aim. To do this, we used the data regarding the electric consumption registered by thirteen buildings located in a university campus in the south of Spain. The empirical comparison of the selected methods on the different data showed that some methods are more suitable than others for this kind of problem. In particular, we show that strategies based on Machine Learning approaches seem to be more suitable for this task.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{IS-2019,

title = {A multivariate approach to the symmetrical uncertainty measure: Application to feature selection problem},

author = {G. Sosa-Cabrera and M. García-Torres and S. Gómez-Guerrero and C.E. Schaerer and F. Divina},

url = {https://www.sciencedirect.com/science/article/pii/S0020025519303603},

doi = {https://doi.org/10.1016/j.ins.2019.04.046},

year  = {2019},

date = {2019-01-01},

journal = {Information Sciences},

volume = {494},

pages = {1--20},

abstract = {In this work we propose an extension of the Symmetrical Uncertainty (SU) measure in order to address the multivariate case, simultaneously acquiring the capability to detect possible correlations and interactions among features. This generalization, denoted Multivariate Symmetrical Uncertainty (MSU), is based on the concepts of Total Correlation (TC) and Mutual Information (MI) extended to the multivariate case. The generalized measure accounts for the total amount of dependency within a set of variables as a single monolithic quantity. Multivariate measures are usually biased due to several factors. To overcome this problem, a mathematical expression is proposed, based on the cardinality of all features, which can be used to calculate the number of samples needed to estimate the MSU without bias at a pre-specified significance level. Theoretical and experimental results on synthetic data show that the proposed sample size expression properly controls the bias. In addition, when the MSU is applied to feature selection on synthetic and real-world data, it has the advantage of adequately capturing linear and nonlinear correlations and interactions, and it can therefore be used as a new feature subset evaluation method.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Genes2019,

title = {Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach},

author = {F. M Delgado-Chaves and F. Gómez-Vela and M. García-Torres and F. Divina and J.L. Vázquez Noguera},

url = {https://www.mdpi.com/2073-4425/10/12/962},

doi = {https://doi.org/10.3390/genes10120962},

year  = {2019},

date = {2019-01-01},

journal = {Genes},

volume = {10},

number = {12},

pages = {962},

abstract = {Gene Networks (GN), have emerged as an useful tool in recent years for the analysis of different diseases in the field of biomedicine. In particular, GNs have been widely applied for the study and analysis of different types of cancer. In this context, Lung carcinoma is among the most common cancer types and its short life expectancy is partly due to late diagnosis. For this reason, lung cancer biomarkers that can be easily measured are highly demanded in biomedical research. In this work, we present an application of gene co-expression networks in the modelling of lung cancer gene regulatory networks, which ultimately served to the discovery of new biomarkers. For this, a robust GN inference was performed from microarray data concomitantly using three different co-expression measures. Results identified a major cluster of genes involved in SRP-dependent co-translational protein target to membrane, as well as a set of 28 genes that were exclusively found in networks generated from cancer samples. Amongst potential biomarkers, genes NCKAP1L and DMD are highlighted due to their implications in a considerable portion of lung and bronchus primary carcinomas. These findings demonstrate the potential of GN reconstruction in the rational prediction of biomarkers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PO:EP-2018,

title = {The Blessing of Dimensionality: Feature Selection Outperforms Functional Connectivity-based Feature Transformation to Classify ADHD Subjects from EEG Patterns of Phase Synchronisation},

author = {E. Pereda and M. García-Torres and B. Melián and S. Ma~nas and L. Méndez and J. González},

url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201660},

doi = {10.1371/journal.pone.0201660},

year  = {2018},

date = {2018-01-01},

journal = {PLoS ONE},

volume = {13},

number = {8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018a,

title = {Gaia Data Release 2. Observational Hertzsprung-Russell diagrams},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/full_html/2018/08/aa32843-18/aa32843-18.html},

doi = {10.1051/0004-6361/201832843},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A10},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018b,

title = {Gaia Data Release 2. Summary of the contents and survey properties},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/full_html/2018/08/aa33051-18/aa33051-18.html},

doi = {10.1051/0004-6361/201833051},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018c,

title = {Gaia Data Release 2. Observations of Solar System Objects},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/abs/2018/08/aa32900-18/aa32900-18.html},

doi = {10.1051/0004-6361/201832900},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A13},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018d,

title = {Gaia Data Release 2. Mapping the milky way disc kinematics},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/abs/2018/08/aa32865-18/aa32865-18.html},

doi = {10.1051/0004-6361/201832865},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A11},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{AA:Gaia-2018e,

title = {Gaia Data Release 2. The celestial reference frame (Gaia-CRF2)},

author = {M. García-Torres and Gaia collaboration},

url = {https://www.aanda.org/articles/aa/full_html/2018/08/aa32916-18/aa32916-18.html},

doi = {10.1051/0004-6361/201832916},

year  = {2018},

date = {2018-01-01},

journal = {Astronomy & Astrophysics},

volume = {616},

number = {A14},

keywords = {},

pubstate = {published},

tppubtype = {article}

}