@conference{HAIS2015,

title = {Improving earthquake prediction with principal component analysis: Application to Chile},

author = {G. Asencio-Cortés and F. Martínez-Álvarez and A. Morales-Esteban and J. Reyes and A. Troncoso},

url = {https://link.springer.com/chapter/10.1007/978-3-319-19644-2_33},

year  = {2015},

date = {2015-01-01},

booktitle = {HAIS 10th International Conference on Hybrid Artificial Intelligence Systems},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{10.1007/978-3-642-29066-4_14b,

title = {Prediction of Mitochondrial Matrix Protein Structures Based on Feature Selection and Fragment Assembly},

author = {G. Asencio-Cortes and J. S. Aguilar-Ruiz and A. E. Marquez-Chamorro and R. Ruiz 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 = {156-167},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

abstract = {Protein structure prediction consists in determining the thre-e-dimensional conformation of a protein based only on its amino acid sequence. This is currently a difficult and significant challenge in structural bioinformatics because these structures are necessary for drug designing. This work proposes a method that reconstructs protein structures from protein fragments assembled according to their physico-chemical similarities, using information extracted from known protein structures. Our prediction system produces distance maps to represent protein structures, which provides more information than contact maps, which are predicted by many proposals in the literature. Most commonly used amino acid physico-chemical properties are hydrophobicity, polarity and charge. In our method, we performed a feature selection on the 544 properties of the AAindex repository, resulting in 16 properties which were used to predictions. We tested our proposal on 74 mitochondrial matrix proteins with a maximum sequence identity of 30% obtained from the Protein Data Bank. We achieved a recall of 0.80 and a precision of 0.79 with an 8-angstrom cut-off and a minimum sequence separation of 7 amino acids. Finally, we compared our system with other relevant proposal on the same benchmark and we achieved a recall improvement of 50.82%. Therefore, for the studied proteins, our method provides a notable improvement in terms of recall.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@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}

}

@conference{10.1007/978-3-642-29066-4_20b,

title = {Short-Range Interactions and Decision Tree-Based Protein Contact Map Predictor},

author = {C. E. Santiesteban-Toca and G. Asencio-Cortes and A. E. Marquez-Chamorro and J. S. Aguilar-Ruiz},

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 = {224-233},

publisher = {Springer Berlin Heidelberg},

address = {Berlin, Heidelberg},

abstract = {In this paper, we focus on protein contact map prediction, one of the most important intermediate steps of the protein folding problem. The objective of this research is to know how short-range interactions can contribute to a system based on decision trees to learn about the correlation among the covalent structures of a protein residues. We propose a solution to predict protein contact maps that combines the use of decision trees with a new input codification for short-range interactions. The method's performance was very satisfactory, improving the accuracy instead using all information of the protein sequence. For a globulin data set the method can predict contacts with a maximal accuracy of 43%. The presented predictive model illustrates that short-range interactions play the predominant role in determining protein structure.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@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}

}

@conference{Cortes2011b,

title = {Predicting protein distance maps according to physicochemical properties},

author = {G. Asencio-Cortes and J. S. Aguilar-Ruiz},

doi = {10.1515/jib-2011-181},

issn = {1613-4516},

year  = {2011},

date = {2011-01-01},

volume = {8},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@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}

}

@conference{10.1007/978-3-642-19914-1_36b,

title = {Prediction of Protein Distance Maps by Assembling Fragments According to Physicochemical Similarities},

author = {G. Asencio-Cortes and J. S. Aguilar-Ruiz and A. E. Marquez-Chamorro},

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 = {271-277},

abstract = {The prediction of protein structures is a current issue of great significance in structural bioinformatics. More specifically, the prediction of the tertiary structure of a protein consists of determining its three-dimensional conformation based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Many approaches cited in the literature use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, implemented with parallel multithreading, a set of 30 physicochemical amino acid properties selected from the AAindex database were used. Several protein tertiary structure prediction methods produce a contact map. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. The results of experiments with several non-homologous protein sets demonstrate the generality of this method and its prediction quality using the amino acid properties considered.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@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}

}

@conference{10.1007/978-3-642-20389-3_7b,

title = {A Nearest Neighbour-Based Approach for Viral Protein Structure Prediction},

author = {G. Asencio-Cortes and J. S. Aguilar-Ruiz and A. E. Marquez-Chamorro},

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 = {69-76},

abstract = {Protein tertiary structure prediction consists of determining the three-dimensional conformation of a protein based solely on its amino acid sequence. This study proposes a method in which protein fragments are assembled according to their physicochemical similarities, using information extracted from known protein structures. Several existing protein tertiary structure prediction methods produce contact maps as their output. Our proposed method produces a distance map, which provides more information about the structure of a protein than a contact map. In addition, many existing approaches use the physicochemical properties of amino acids, generally hydrophobicity, polarity and charge, to predict structure. In our method, we used three different physicochemical properties of amino acids obtained from the literature. Using this method, we performed tertiary structure predictions on 63 viral capsid proteins with a maximum identity of 30% obtained from the Protein Data Bank. We achieved a precision of 0.75 with an 8-angstrom cut-off and a minimum sequence separation of 7 amino acids. Thus, for the studied proteins, our results provide a notable improvement over those of other methods.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{10.1007/978-3-642-20389-3_16b,

title = {A Decision Tree-Based Method for Protein Contact Map Prediction},

author = {C. E. Santiesteban-Toca and A. E. Marquez-Chamorro and G. Asencio-Cortes and J. S. Aguilar-Ruiz},

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 = {153-158},

abstract = {In this paper, we focus on protein contact map prediction. We describe a method where contact maps are predicted using decision tree-based model. The algorithm includes the subsequence information between the couple of analyzed amino acids. In order to evaluate the method generalization capabilities, we carry out an experiment using 173 non-homologous proteins of known structures. Our results indicate that the method can assign protein contacts with an average accuracy of 0.34, superior to the 0.25 obtained by the FNETCSS method. This shows that our algorithm improves the accuracy with respect to the methods compared, especially with the increase of protein length.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{10.1007/978-3-642-16001-1_31b,

title = {Alpha Helix Prediction Based on Evolutionary Computation},

author = {A. E. Marquez-Chamorro and F. Divina and J. S. Aguilar-Ruiz and G. Asencio-Cortes},

editor = {Dijkstra, Tjeerd M. H. and Tsivtsivadze, Evgeni and Marchiori, Elena and Heskes, Tom},

isbn = {978-3-642-16001-1},

year  = {2010},

date = {2010-01-01},

booktitle = {Pattern Recognition in Bioinformatics},

pages = {358-367},

abstract = {Multiple approaches have been developed in order to predict the protein secondary structure. In this paper, we propose an approach to such a problem based on evolutionary computation. The proposed approach considers various amino acids properties in order to predict the secondary structure of a protein. In particular, we will consider the hydrophobicity, the polarity and the charge of amino acids. In this study, we focus on predicting a particular kind of secondary structure: $alpha$-helices. The results of our proposal will be a set of rules that will identify the beginning or the end of such a structure.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ref3b,

title = {Importancia de las Propiedades Fisico-Quimicas de los Aminoacidos en la Prediccion de Estructuras de Proteinas usando Vecinos mas Cercanos},

author = {G. Asencio-Cortes and J. S. Aguilar-Ruiz},

year  = {2010},

date = {2010-01-01},

booktitle = {XV Congreso Español sobre Tecnologias y Logica Fuzzy, ISBN: 978-84-92944-02-6.},

pages = {pp. 459-464},

abstract = {La prediccion de estructuras de proteinas es actualmente un importante campo de investigacion dentro de la bioinformatica. En esta area, existen numerosos estudios realizados en los que se ha usado la informacion de la separacion entre los aminoacidos de una cadena para predecir la estructura de las proteinas, utilizandose en otros trabajos ciertas propiedades fisico-quimicas de aminoacidos. En este trabajo se han usado ambas informaciones y se ha estudiado como influyen en la prediccion de estructuras de proteinas empleando el algoritmo de vecinos mas cercanos. Hemos comprobado que la informacion proporcionada por las propiedades fisico-quimicas es de mayor interes que la separacion, obteniendose mejores tasas de acierto. Se han realizado cuatro experimentos en los que se ha usado como atributos, la separacion entre aminoacidos y un conjunto determinado de propiedades fisico-quimicas de los mismos y, como ejemplos, todas las subsecuencias posibles encontradas en un conjunto de mas de 5000 proteinas reales. Finalmente se demuestra empiricamente que la separacion entre aminoacidos, ampliamente usada en la literatura, puede ser reemplazada por propiedades fisico-quimicas de amino-acidos, produciendo mejores predicciones. La tasa de acierto conseguida usando solo la separacion esta en torno al 59%, ascendiendo este valor hasta el 79% al usarse un conjunto de propiedades fisico-quimicas de aminoacidos.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ref4b,

title = {Prediccion de Estructuras de Proteinas mediante Vecinos mas Cercanos usando Caracteristicas Inherentes a los Aminoacidos},

author = {G. Asencio-Cortes and J. S. Aguilar-Ruiz},

year  = {2009},

date = {2009-01-01},

booktitle = {II Workshop Español sobre Extraccion y Validacion de Conocimientos en Base de Datos Biomedicas},

pages = {1-10},

abstract = {En este trabajo se ha estudiado como influye la separacion 

entre aminoacidos de una proteína y ciertas características inherentes a 

su naturaleza en la prediccion de estructuras de proteínas mediante un 

algoritmo de vecinos mas cercanos. En el proceso de prediccion se han 

generado todas las subsecuencias posibles de aminoacidos procedentes 

de un conjunto de proteínas reales. Se demuestra empíricamente que la 

separacion entre aminoacidos produce peores predicciones que las propiedades 

naturales inherentes a los mismos. Esto plantea la hipotesis de 

que la informacion que suministra dicha separacion se encuentra implícita 

en la informacion proporcionada por las propiedades físico-químicas, 

ya que se han obtenido iguales resultados tanto en presencia como en 

ausencia del atributo separacion.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}