Citations and socials
Miguel García Torres is an associate professor in the Escuela Politécnica Superior of the Universidad Pablo de Olavide. He received the BS degree in physics and the PhD degree in computer science from the Universidad de La Laguna, Tenerife, Spain, in 2001 and 2007, respectively. After obtaining the doctorate he held a postoc position in the Laboratory for Space Astrophysics and Theoretical Physics at the National institute of Aerospace Technology (INTA). There, he joined in the Gaia mission from the European Space Agency (ESA) and started to participate in the Gaia Data Processing and Analysis Consortium (DPAC) as a member of “Astrophysical Parameters”, Coordination Unit (CU8). He has been involved in the “Object Clustering Analysis” (OCA) Development Unit since then. His research areas of interests include machine learning, metaheuristics, big data, time series forecasting, bioinformatics and astrostatistics.
Publications
2019 |
M. García-Torres and D. Becerra-Alonso and F. A Gómez-Vela and F. Divina and I. López Cobo and F. Martínez-Álvarez Analysis of Student Achievement Scores: A Machine Learning Approach Conference ICEUTE 10th International Conference on EUropean Transnational Education, Advances in Intelligent Systems and Computing 2019. @conference{Garcia2019, title = {Analysis of Student Achievement Scores: A Machine Learning Approach}, author = {M. García-Torres and D. Becerra-Alonso and F. A Gómez-Vela and F. Divina and I. López Cobo and F. Martínez-Álvarez}, url = {https://link.springer.com/chapter/10.1007/978-3-030-20005-3_28}, year = {2019}, date = {2019-01-01}, booktitle = {ICEUTE 10th International Conference on EUropean Transnational Education}, pages = {275-284}, series = {Advances in Intelligent Systems and Computing}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
V.E. Jiménez Chaves and M. García-Torres and J. Luis Vázquez Noguera and C.D. Cabrera Oviedo and A.P. Riego Esteche and F. Divina and M. Marrufo-Vázquez International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019), 2019. @conference{Chaves2019, title = {Analysis of Teacher Training in Mathematics in Paraguay’s Elementary Education System Using Machine Learning Techniques}, author = {V.E. Jiménez Chaves and M. García-Torres and J. Luis Vázquez Noguera and C.D. Cabrera Oviedo and A.P. Riego Esteche and F. Divina and M. Marrufo-Vázquez}, url = {https://link.springer.com/chapter/10.1007/978-3-030-20005-3_29}, year = {2019}, date = {2019-01-01}, booktitle = {International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019)}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
F. M Delgado-Chaves and F. Gómez-Vela and M. García-Torres and F. Divina and J. Luis Vázquez Noguera Computational Inference of Gene Co-Expression Networks for the identification of Lung Carcinoma Biomarkers: An Ensemble Approach Journal Article Genes, 10 (12), pp. 962, 2019. @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. Luis 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} } 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. |
F. Gómez-Vela and F. M Delgado-Chaves and D.S. Rodríguez-Baena and M. García-Torres and F. Divina Ensemble and Greedy Approach for the Reconstruction of Large Gene Co-Expression Networks Journal Article Entropy, 21 (12), pp. 1139, 2019. @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} } 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. |
F. Divina and M. García-Torres and F. Goméz-Vela and J.L. Vázquez Noguera A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings Journal Article Applied Sciences, 12 (10), pp. 1934, 2019. @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} } 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. |
G. Sosa-Cabrera and M. García-Torres and S. Gómez-Guerrero and C.E. Schaerer and F. Divina A multivariate approach to the symmetrical uncertainty measure: Application to feature selection problem Journal Article Information Sciences, 494 , pp. 1–20, 2019. @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} } 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. |
2018 |
P. Manuel Martínez-García and M. García-Torres and F. Divina and F. Gómez-Vela and F. Cortés-Ledesma International Conference on the Applications of Evolutionary Computation, 2018. @conference{Top2B2018b, title = {Analysis of Relevance and Redundance on Topoisomerase 2b (TOP2B) Binding Sites: A Feature Selection Approach}, author = {P. Manuel Martínez-García and M. García-Torres and F. Divina and F. Gómez-Vela and F. Cortés-Ledesma}, url = {https://link.springer.com/chapter/10.1007/978-3-319-77538-8_7}, year = {2018}, date = {2018-01-01}, booktitle = {International Conference on the Applications of Evolutionary Computation}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
G. Sosa-Cabrera and M. García-Torres and S. Gómez Guerrero and C.E. Schaerer and F. Divina Understanding a multivariate semi-metric in the search strategies for attributes subset selection Conference Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, 2018. @conference{Sosa2018b, title = {Understanding a multivariate semi-metric in the search strategies for attributes subset selection}, author = {G. Sosa-Cabrera and M. García-Torres and S. Gómez Guerrero and C.E. Schaerer and F. Divina}, url = {https://proceedings.sbmac.emnuvens.com.br/sbmac/article/view/2506}, year = {2018}, date = {2018-01-01}, booktitle = {Proceeding Series of the Brazilian Society of Computational and Applied Mathematics}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
F. Divina and A. Gilson and F. Goméz-Vela and M. García-Torres and J. F. Torres Stacking ensemble learning for short-term electricity consumption forecasting Journal Article Energies, 11 (4), pp. 949, 2018. @article{Energy2018, title = {Stacking ensemble learning for short-term electricity consumption forecasting}, author = {F. Divina and A. Gilson and F. Goméz-Vela and M. García-Torres and J. F. Torres}, url = {https://www.mdpi.com/1996-1073/11/4/949}, doi = {https://doi.org/10.3390/en11040949}, year = {2018}, date = {2018-01-01}, journal = {Energies}, volume = {11}, number = {4}, pages = {949}, abstract = {The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.}, keywords = {}, pubstate = {published}, tppubtype = {article} } The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem. |
E. Pereda and M. García-Torres and B. Melián and S. Ma~nas and L. Méndez and J. González PLoS ONE, 13 (8), 2018. @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} } |
M. García-Torres and Gaia collaboration Gaia Data Release 2. Observational Hertzsprung-Russell diagrams Journal Article Astronomy & Astrophysics, 616 (A10), 2018. @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} } |
M. García-Torres and Gaia collaboration Gaia Data Release 2. Summary of the contents and survey properties Journal Article Astronomy & Astrophysics, 616 (A1), 2018. @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} } |
M. García-Torres and Gaia collaboration Gaia Data Release 2. Observations of Solar System Objects Journal Article Astronomy & Astrophysics, 616 (A13), 2018. @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} } |
M. García-Torres and Gaia collaboration Gaia Data Release 2. Mapping the milky way disc kinematics Journal Article Astronomy & Astrophysics, 616 (A11), 2018. @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} } |
M. García-Torres and Gaia collaboration Gaia Data Release 2. The celestial reference frame (Gaia-CRF2) Journal Article Astronomy & Astrophysics, 616 (A14), 2018. @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} } |