@article{martinez2015,
title = {A Novel Method for Seismogenic Zoning Based on Triclustering: Application to the Iberian Peninsula},
author = {F. Martínez-Álvarez and D. Gutiérrez-Avilés and A. Morales-Esteban and J. Reyes and J. L. Amaro-Mellado and C. Rubio-Escudero},
url = {https://www.mdpi.com/1099-4300/17/7/5000},
doi = {10.3390/e17075000},
year = {2015},
date = {2015-01-01},
journal = {Entropy},
volume = {17},
number = {7},
pages = {5000-5021},
abstract = {A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas of spread and low seismic activity. Traditional zoning methods are based on the availabl seismic catalog and the geological structures. It is admitted that thermal and resistant parameters of the crust provide better criteria for zoning. Nonetheless, the working out of the rheological profiles causes a great uncertainty. This has generated inconsistencies, as different zones have been proposed for the same area. A new method for seismogenic zoning by means of triclustering is proposed in this research. The main advantage is that it is solely based on seismic data. Almost no human decision is made, and therefore, the method is nearly non-biased. To assess its performance, the method has been applied to the Iberian Peninsula, which is characterized by the occurrence of small to moderate magnitude earthquakes. The catalog of the National Geographic Institute of Spain has been used. The output map is checked for validity with the geology. Moreover, a geographic information system has been used for two purposes. First, the obtained zones have been depicted within it. Second, the data have been used to calculate the seismic parameters (b-value, annual rate). Finally, the results have been compared to Kohonen’s self-organizing maps.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Gutierrez-Aviles2014,
title = {Mining 3D Patterns from Gene Expression Temporal Data: A New Tricluster Evaluation Measure},
author = {D. Gutiérrez-Avilés and C. Rubio-Escudero},
url = {http://www.hindawi.com/journals/tswj/2014/624371/},
doi = {10.1155/2014/624371},
year = {2014},
date = {2014-01-01},
journal = {The Scientific World Journal},
volume = {2014},
pages = {1-16},
abstract = {Microarrays have revolutionized biotechnological research. The analysis of new data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are applied to create groups of genes that exhibit a similar behavior. Biclustering emerges as a valuable tool for microarray data analysis since it relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. However, if a third dimension appears in the data, triclustering is the appropriate tool for the analysis. This occurs in longitudinal experiments in which the genes are evaluated under conditions at several time points. All clustering, biclustering, and triclustering techniques guide their search for solutions by a measure that evaluates the quality of clusters. We present an evaluation measure for triclusters called Mean Square Residue 3D. This measure is based on the classic biclustering measure Mean Square Residue. Mean Square Residue 3D has been applied to both synthetic and real data and it has proved to be capable of extracting groups of genes with homogeneous patterns in subsets of conditions and times, and these groups have shown a high correlation level and they are also related to their functional annotations extracted from the Gene Ontology project.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{Gutierrez-Aviles2014b,
title = {LSL: A new measure to evaluate triclusters},
author = {D. Gutiérrez-Avilés and C. Rubio-Escudero},
url = {http://ieeexplore.ieee.org/document/6999244/},
year = {2014},
date = {2014-01-01},
booktitle = {2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@article{GUTIERREZAVILES201442,
title = {TriGen: A genetic algorithm to mine triclusters in temporal gene expression data},
author = {D. Gutiérrez-Avilés and C. Rubio-Escudero and F. Martínez-Álvarez and J.C. Riquelme},
url = {http://www.sciencedirect.com/science/article/pii/S0925231213011004},
doi = {10.1016/j.neucom.2013.03.061},
year = {2014},
date = {2014-01-01},
journal = {Neurocomputing},
volume = {132},
pages = {42-53},
abstract = {Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques are widely applied to create groups of genes that exhibit a similar behavior under the conditions tested. Biclustering emerges as an improvement of classical clustering since it relaxes the constraints for grouping genes to be evaluated only under a subset of the conditions and not under all of them. However, this technique is not appropriate for the analysis of longitudinal experiments in which the genes are evaluated under certain conditions at several time points. We present the TriGen algorithm, a genetic algorithm that finds triclusters of gene expression that take into account the experimental conditions and the time points simultaneously. We have used TriGen to mine datasets related to synthetic data, yeast (Saccharomyces cerevisiae) cell cycle and human inflammation and host response to injury experiments. TriGen has proved to be capable of extracting groups of genes with similar patterns in subsets of conditions and times, and these groups have shown to be related in terms of their functional annotations extracted from the Gene Ontology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@workshop{Aviles2012,
title = {Finding motifs in DNA sequences},
author = {D. Gutiérrez-Avilés and F. Martínez-Álvarez and C. Rubio-Escudero and J. C. Riquelme},
year = {2012},
date = {2012-01-01},
booktitle = {Spanish Conference on Technologies and Fuzzy Logic (ESTYLF'12)},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}

@conference{Gutierrez-Aviles2011a,
title = {Revisiting the yeast cell cycle problem with the improved TriGen algorithm},
author = {D. Gutiérrez-Avilés and C. Rubio-Escudero and J. C. Riquelme},
url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6089642 http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6089642 http://ieeexplore.ieee.org/document/6089642/},
year = {2011},
date = {2011-01-01},
booktitle = {2011 Third World Congress on Nature and Biologically Inspired Computing},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{Gutierrez-Aviles2011b,
title = {Unravelling the Yeast Cell Cycle Using the TriGen Algorithm},
author = {D. Gutiérrez-Avilés and C. Rubio-Escudero and J. C. Riquelme},
url = {https://link.springer.com/chapter/10.1007%2F978-3-642-25274-7_16},
year = {2011},
date = {2011-01-01},
booktitle = {Advances in Artificial Intelligence},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}