Citations and socials
Federico Divina obtained his Ph.D. in Artificial Intelligence from the Vrije Universiteit of Amsterdam, and after that he worked as a postdoc at the University of Tilburg, within the European project NEWTIES. In 2006 he moved to the Pablo de Olavide University.
He has been working on knowledge extraction since his Ph.D. thesis at the Vrije Universiteit of Amsterdam. He has extensive experience in the application of Machine Learning, especially techniques based on Soft Computing, for the extraction of knowledge from massive data.
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. |
E.L. Mangas and A. Rubio and R. Álvarez-Marín and G. Labrador-Herrera and J. Pachón and M. Eugenia Pachón-Ibáñez and F. Divina and A.J. Pérez-Pulido Microbial Genomics, pp. mgen000309, 2019. @article{MG2019, title = {Pangenome of Acinetobacter baumannii uncovers two groups of genomes, one of them with genes involved in CRISPR/Cas defence systems associated with the absence of plasmids and exclusive genes for biofilm formation}, author = {E.L. Mangas and A. Rubio and R. Álvarez-Marín and G. Labrador-Herrera and J. Pachón and M. Eugenia Pachón-Ibáñez and F. Divina and A.J. Pérez-Pulido}, url = {https://www.microbiologyresearch.org/content/journal/mgen/10.1099/mgen.0.000309}, doi = {https://doi.org/10.1099/mgen.0.000309}, year = {2019}, date = {2019-01-01}, journal = {Microbial Genomics}, pages = {mgen000309}, abstract = {Acinetobacter baumannii is an opportunistic bacterium that causes hospital-acquired infections with a high mortality and morbidity, since there are strains resistant to virtually any kind of antibiotic. The chase to find novel strategies to fight against this microbe can be favoured by knowledge of the complete catalogue of genes of the species, and their relationship with the specific characteristics of different isolates. In this work, we performed a genomics analysis of almost 2500 strains. Two different groups of genomes were found based on the number of shared genes. One of these groups rarely has plasmids, and bears clustered regularly interspaced short palindromic repeat (CRISPR) sequences, in addition to CRISPR-associated genes (cas genes) or restriction-modification system genes. This fact strongly supports the lack of plasmids. Furthermore, the scarce plasmids in this group also bear CRISPR sequences, and specifically contain genes involved in prokaryotic toxin–antitoxin systems that could either act as the still little known CRISPR type IV system or be the precursors of other novel CRISPR/Cas systems. In addition, a limited set of strains present a new cas9-like gene, which may complement the other cas genes in inhibiting the entrance of new plasmids into the bacteria. Finally, this group has exclusive genes involved in biofilm formation, which would connect CRISPR systems to the biogenesis of these bacterial resistance structures.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Acinetobacter baumannii is an opportunistic bacterium that causes hospital-acquired infections with a high mortality and morbidity, since there are strains resistant to virtually any kind of antibiotic. The chase to find novel strategies to fight against this microbe can be favoured by knowledge of the complete catalogue of genes of the species, and their relationship with the specific characteristics of different isolates. In this work, we performed a genomics analysis of almost 2500 strains. Two different groups of genomes were found based on the number of shared genes. One of these groups rarely has plasmids, and bears clustered regularly interspaced short palindromic repeat (CRISPR) sequences, in addition to CRISPR-associated genes (cas genes) or restriction-modification system genes. This fact strongly supports the lack of plasmids. Furthermore, the scarce plasmids in this group also bear CRISPR sequences, and specifically contain genes involved in prokaryotic toxin–antitoxin systems that could either act as the still little known CRISPR type IV system or be the precursors of other novel CRISPR/Cas systems. In addition, a limited set of strains present a new cas9-like gene, which may complement the other cas genes in inhibiting the entrance of new plasmids into the bacteria. Finally, this group has exclusive genes involved in biofilm formation, which would connect CRISPR systems to the biogenesis of these bacterial resistance structures. |
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. |
2014 |
A. E. Marquez-Chamorro and G. Asencio-Cortes and F. Divina and J. S. Aguilar-Ruiz Evolutionary decision rules for predicting protein contact maps Journal Article Pattern Analysis and Applications, 4 (17), pp. 725-737, 2014, ISSN: 1433-7541. @article{Marquez-Chamorro2014, title = {Evolutionary decision rules for predicting protein contact maps}, author = {A. E. Marquez-Chamorro and G. Asencio-Cortes and F. Divina and J. S. Aguilar-Ruiz}, doi = {10.1007/s10044-012-0297-3}, issn = {1433-7541}, year = {2014}, date = {2014-01-01}, journal = {Pattern Analysis and Applications}, volume = {4}, number = {17}, pages = {725-737}, abstract = {Protein structure prediction is currently one of the main open challenges in Bioinformatics. The protein contact map is an useful, and commonly used, representation for protein 3D structure and represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. In this work, we propose a multi-objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties to predict contacts. We present results obtained by our approach on four different protein data sets. A statistical study was also performed to extract valid conclusions from the set of prediction rules generated by our algorithm. Results obtained confirm the validity of our proposal.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Protein structure prediction is currently one of the main open challenges in Bioinformatics. The protein contact map is an useful, and commonly used, representation for protein 3D structure and represents binary proximities (contact or non-contact) between each pair of amino acids of a protein. In this work, we propose a multi-objective evolutionary approach for contact map prediction based on physico-chemical properties of amino acids. The evolutionary algorithm produces a set of decision rules that identifies contacts between amino acids. The rules obtained by the algorithm impose a set of conditions based on amino acid properties to predict contacts. We present results obtained by our approach on four different protein data sets. A statistical study was also performed to extract valid conclusions from the set of prediction rules generated by our algorithm. Results obtained confirm the validity of our proposal. |
2012 |
A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and J. Bacardit and G. Asencio-Cortes and C. E. Santiesteban-Toca A NSGA-II Algorithm for the Residue-Residue Contact Prediction Conference Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012, ISBN: 978-3-642-29066-4. @conference{10.1007/978-3-642-29066-4_21b, title = {A NSGA-II Algorithm for the Residue-Residue Contact Prediction}, author = {A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and J. Bacardit and G. Asencio-Cortes and C. E. Santiesteban-Toca}, editor = {Giacobini, Mario and Vanneschi, Leonardo and Bush, William S.}, isbn = {978-3-642-29066-4}, year = {2012}, date = {2012-01-01}, booktitle = {Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics}, pages = {234-244}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {We present a multi-objective evolutionary approach to predict protein contact maps. The algorithm provides a set of rules, inferring whether there is contact between a pair of residues or not. Such rules are based on a set of specific amino acid properties. These properties determine the particular features of each amino acid represented in the rules. In order to test the validity of our proposal, we have compared results obtained by our method with results obtained by other classification methods. The algorithm shows better accuracy and coverage rates than other contact map predictor algorithms. A statistical analysis of the resulting rules was also performed in order to extract conclusions of the protein folding problem.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } We present a multi-objective evolutionary approach to predict protein contact maps. The algorithm provides a set of rules, inferring whether there is contact between a pair of residues or not. Such rules are based on a set of specific amino acid properties. These properties determine the particular features of each amino acid represented in the rules. In order to test the validity of our proposal, we have compared results obtained by our method with results obtained by other classification methods. The algorithm shows better accuracy and coverage rates than other contact map predictor algorithms. A statistical analysis of the resulting rules was also performed in order to extract conclusions of the protein folding problem. |
2011 |
A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and G. Asencio-Cortes A multi-objective genetic algorithm for the Protein Structure Prediction Conference 2011 11th International Conference on Intelligent Systems Design and Applications, 2011, ISSN: 2164-7151. @conference{6121803b, title = {A multi-objective genetic algorithm for the Protein Structure Prediction}, author = {A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and G. Asencio-Cortes}, doi = {10.1109/ISDA.2011.6121803}, issn = {2164-7151}, year = {2011}, date = {2011-11-01}, booktitle = {2011 11th International Conference on Intelligent Systems Design and Applications}, pages = {1086-1090}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and G. Asencio-Cortes Residue-Residue Contact Prediction Based on Evolutionary Computation Conference 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011), Springer Berlin Heidelberg, 2011, ISBN: 978-3-642-19914-1. @conference{10.1007/978-3-642-19914-1_37b, title = {Residue-Residue Contact Prediction Based on Evolutionary Computation}, author = {A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and G. Asencio-Cortes}, editor = {Rocha, Miguel P. and Rodríguez, Juan M. Corchado and Fdez-Riverola, Florentino and Valencia, Alfonso}, isbn = {978-3-642-19914-1}, year = {2011}, date = {2011-01-01}, booktitle = {5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011)}, pages = {279-283}, publisher = {Springer Berlin Heidelberg}, abstract = {In this study, a novel residue-residue contacts prediction approach based on evolutionary computation is presented. The prediction is based on four amino acids properties. In particular, we consider the hydrophobicity, the polarity, the charge and residues size. The prediction model consists of a set of rules that identifies contacts between amino acids.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In this study, a novel residue-residue contacts prediction approach based on evolutionary computation is presented. The prediction is based on four amino acids properties. In particular, we consider the hydrophobicity, the polarity, the charge and residues size. The prediction model consists of a set of rules that identifies contacts between amino acids. |
A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and G. Asencio-Cortes An Evolutionary Approach for Protein Contact Map Prediction Conference Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, 2011, ISBN: 978-3-642-20389-3. @conference{10.1007/978-3-642-20389-3_10b, title = {An Evolutionary Approach for Protein Contact Map Prediction}, author = {A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and G. Asencio-Cortes}, editor = {Pizzuti, Clara and Ritchie, Marylyn D. and Giacobini, Mario}, isbn = {978-3-642-20389-3}, year = {2011}, date = {2011-01-01}, booktitle = {Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics}, pages = {101-110}, abstract = {In this study, we present a residue-residue contact prediction approach based on evolutionary computation. Some amino acid properties are employed according to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is characterized by these four properties. We also include a statistical study for the propensities of contacts between each pair of amino acids, according to their types, hydrophobicity and polarity. Different experiments were also performed to determine the best selection of properties for the structure prediction among the cited properties.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In this study, we present a residue-residue contact prediction approach based on evolutionary computation. Some amino acid properties are employed according to their importance in the folding process: hydrophobicity, polarity, charge and residue size. Our evolutionary algorithm provides a set of rules which determine different cases where two amino acids are in contact. A rule represents two windows of three amino acids. Each amino acid is characterized by these four properties. We also include a statistical study for the propensities of contacts between each pair of amino acids, according to their types, hydrophobicity and polarity. Different experiments were also performed to determine the best selection of properties for the structure prediction among the cited properties. |