Carlos D. Barranco

@article{Rodríguez-Baena2020,

title = {Identifying livestock behavior patterns based on accelerometer dataset},

author = {D. Rodríguez-Baena and F. Gómez-Vela and M. García-Torres and F. Divina and C. D. Barranco and N. Díaz-Díaz and M. Jiménez and G. Montalvo},

url = {http://www.sciencedirect.com/science/article/pii/S1877750318311359},

doi = {10.1016/j.jocs.2020.101076},

issn = {1877-7503},

year  = {2020},

date = {2020-03-01},

journal = {Journal of Computational Science},

volume = {41},

pages = {101076},

abstract = {In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However, in large breeding farms this may not represent the best approach, since, in this way, not all the animals can be effectively monitored all the time. Moreover, the traditional approach heavily rely on human experience, which cannot be always taken for granted. To this aim, in this paper, we propose a new method for automatically detecting activity and inactivity time periods of animals, as a behavior indicator of livestock. In order to do this, we collected data with sensors located in the body of the animals to be analyzed. In particular, the reliability of the method was tested with data collected on Iberian pigs and calves. Results confirm that the proposed method can help breeders in detecting activity and inactivity periods for large livestock farming.},

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@article{Díaz-Montaña2019,

title = {Development and use of a Cytoscape app for GRNCOP2},

author = {J. J. Díaz-Montaña and N. Díaz-Díaz and C. D. Barranco and I. Ponzoni},

url = {http://www.sciencedirect.com/science/article/pii/S0169260718313592},

doi = {10.1016/j.cmpb.2019.05.030},

issn = {0169-2607},

year  = {2019},

date = {2019-08-01},

journal = {Computer Methods and Programs in Biomedicine},

volume = {177},

pages = {211-218},

abstract = {Background and Objective: Gene regulatory networks (GRNs) are essential for understanding most molecular processes. In this context, the so-called model-free approaches have an advantage modeling the complex topologies behind these dynamic molecular networks, since most GRNs are difficult to map correctly by any other mathematical model. Abstract model-free approaches, also known as rule-based extraction methods, offer valuable benefits when performing data-driven analysis; such as requiring the least amount of data and simplifying the inference of large models at a faster analysis speed. In particular, GRNCOP2 is a combinatorial optimization method with an adaptive criterion for the discretization of gene expression data and high performance, in contrast to other rule-based extraction methods for discovering GRNs. However, the analysis of the large relational structures of the networks inferred by GRNCOP2 requires the support of effective tools for interactive network visualization and topological analysis of the extracted associations. This need motivated the possibility of integrating GRNCOP2 in the Cytoscape ecosystem in order to benefit from Cytoscapes core functionality, as well as all the other apps in its ecosystem.



Methods: In this paper, we introduce the implementation of a GRNCOP2 Cytoscape app. This incorporation to Cytoscape platform includes new functionality for GRN visualizations, dynamic user-interaction and integration with other apps for topological analysis of the networks.



Results: In order to demonstrate the usefulness of integrating GRNCOP2 in Cytoscape, the new app was used to tackle a novel use case for GRNCOP2: the analysis of crosstalk between pathways. In this regard, datasets associated with Alzheimer’s disease (AD) were analyzed using GRNCOP2 app and other apps of the Cytoscape ecosystem by performing a topological analysis of the AD progression and its synchronization with the Ubiquitin Mediated Proteolysis pathway. Finally, the biological relevance of the findings achieved by this new app were evaluated by searching for evidence in the literature.



Conclusions: The proposed crosstalk analysis with the new GRNCOP2 app focused on assessing the phase of the Alzheimer’s disease progression where the coordination with the Ubiquitin Mediated Proteolysis pathway increase, and identifying the genes that explain the signalling between these cellular processes. Both questions were explored by topological contrastive analysis of the GRNs generated for the GRNCOP2 app, where several facilities of Cytoscape were exploited. The topological patterns inferred by this new App have been consistent with biological evidence reported in the scientic literature, illustrating the effectiveness of using this new GRNCOP2 App in pathway analysis.



Availability: The GRNCOP2 App is freely available at the official Cytoscape app store: http://apps.cytoscape.org/apps/grncop2},

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@article{Medina2017,

title = {Indexing techniques to improve the performance of necessity-based fuzzy queries using classical indexing of RDBMS},

author = {J. M. Medina and C. D. Barranco and O. Pons},

url = {http://www.sciencedirect.com/science/article/pii/S0165011417303482},

doi = {10.1016/j.fss.2017.09.008},

issn = {0165-0114},

year  = {2018},

date = {2018-11-01},

journal = {Fuzzy Sets and Systems},

volume = {351},

pages = {90-107},

abstract = {It is widely known that the most effective way to implement a fuzzy database is to use a classical Relational Database Management System (RDBMS) as the basis. All these systems provide several kinds of indexing methods to improve the execution time of classical queries, but they are useless when directly applied to fuzzy queries. For this reason, in this work we propose and evaluate several fuzzy indexing techniques implemented over the indexing techniques available on classical RDBMS in order to enhance flexible queries when based on the necessity measure. As the results show, the best evaluated fuzzy indexing techniques can be implemented on top of classical RDBMS.},

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@article{Medina2016,

title = {Evaluation of Indexing Strategies for Possibilistic Queries Based on Indexing Techniques Available in Traditional RDBMS},

author = {J. M. Medina and C. D. Barranco and O. Pons},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/int.21820

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.21820},

doi = {10.1002/int.21820},

year  = {2016},

date = {2016-10-18},

journal = {International Journal of Intelligent Systems},

volume = {31},

number = {12},

pages = {1135-1165},

abstract = {A common way to implement a fuzzy database is on top of a classical relational database management systems (RDBMS). Given that almost all RDBMS provide indexing mechanisms to enhance classical query processing performance, finding ways to use these mechanisms to enhance the performance of flexible query processing is of enormous interest. This work proposes and evaluates a set of indexing strategies, implemented exclusively on top of classical RDBMS indexing structures, designed to improve flexible query processing performance, focusing in the case of possibilities queries. Results show the best indexing strategies for different data a query scenarios, offering effective ways to implement fuzzy data indexes on top of a classical RDBMS.},

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@article{Gómez-Vela2016,

title = {Incorporating biological knowledge for construction of fuzzy networks of gene associations},

author = {F. Gómez-Vela and C. D. Barranco and N. Díaz-Díaz },

url = {http://www.sciencedirect.com/science/article/pii/S1568494616300011},

doi = {10.1016/j.asoc.2016.01.014},

issn = {1568-4946},

year  = {2016},

date = {2016-05-01},

journal = {Applied Soft Computing},

volume = {42},

pages = {144-155},

abstract = {Gene association networks have become one of the most important approaches to modelling of biological processes by means of gene expression data. According to the literature, co-expression-based methods are the main approaches to identification of gene association networks because such methods can identify gene expression patterns in a dataset and can determine relations among genes. These methods usually have two fundamental drawbacks. Firstly, they are dependent on quality of the input dataset for construction of reliable models because of the sensitivity to data noise. Secondly, these methods require that the user select a threshold to determine whether a relation is biologically relevant. Due to these shortcomings, such methods may ignore some relevant information. We present a novel fuzzy approach named FyNE (Fuzzy NEtworks) for modelling of gene association networks. FyNE has two fundamental features. Firstly, it can deal with data noise using a fuzzy-set-based protocol. Secondly, the proposed approach can incorporate prior biological knowledge into the modelling phase, through a fuzzy aggregation function. These features help to gain some insights into doubtful gene relations. The performance of FyNE was tested in four different experiments. Firstly, the improvement offered by FyNE over the results of a co-expression-based method in terms of identification of gene networks was demonstrated on different datasets from different organisms. Secondly, the results produced by FyNE showed its low sensitivity to noise data in a randomness experiment. Additionally, FyNE could infer gene networks with a biological structure in a topological analysis. Finally, the validity of our proposed method was confirmed by comparing its performance with that of some representative methods for identification of gene networks},

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@article{Medina2014,

title = {A Fuzzy Temporal Object-Relational Database: Model and Implementation},

author = {J. M. Medina and J. E. Pons and C. D. Barranco and O. Pons},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/int.21666

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.21666},

doi = {10.1002/int.21666},

year  = {2014},

date = {2014-06-24},

journal = {International Journal of Intelligent Systems},

volume = {29},

number = {9},

pages = {836-863},

abstract = {Abstract In real world, some data have a specific temporal validity that must be appropiately managed. To deal with this kind of data, several proposals of temporal databases have been introduced. Moreover, time can also be affected by imprecision, vagueness, and/or uncertainty, since human beings manage time using temporal indications and temporal notions, which may also be imprecise. For this reason, information systems require appropriate support to accomplish this task. In this work, we present a novel possibilistic valid time model for fuzzy databases including the data structures, the integrity constraints, and the DML. Together with this model, we also present its implementation by means of a fuzzy valid time support module on top of a fuzzy object-relational database system. The integration of these modules allows to perform queries that combines fuzzy valid time constraints together with fuzzy predicates. Besides, the model and implementation proposed support the crisp valid time model as a particular case of the fuzzy valid time support provided.},

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@article{Barranco2013,

title = {Building a Fuzzy Valid Time Support Module on a Fuzzy Object-Relational Database},

author = {C. D. Barranco and J. M. Medina and J. E. Pons and O. Pons},

doi = {10.1007/978-3-642-40769-7_39},

isbn = {978-3-642-40769-7},

year  = {2013},

date = {2013-05-01},

journal = {Flexible Query Answering Systems},

pages = {447-458},

abstract = {In this work we present the implementation of a Fuzzy Valid Time Support Module on top of a Fuzzy Object-Relational Database System, based on a model to deal with imprecision in valid-time databases. The integration of these modules allows to perform queries that combines fuzzy valid time constraints with fuzzy predicates. Both modules can be deployed in Oracle Relational Database Management System 10.2 and higher. The module implements the mechanisms that overload the SQL sentences: Insert, Update, Delete and Select to allow fuzzy temporal handling. The implementation described supports the crisp valid time model as a particular case of its fuzzy valid time support provided.},

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@article{Medina2012,

title = {On Modeling the Behavior of Comparators for Complex Fuzzy Objects in a Fuzzy Object-Relational Database Management System},

author = {J. M. Medina and C. D. Barranco and J. R. Campaña and S. J. Castillo},

doi = {10.1080/18756891.2012.718159},

issn = {1875-6883},

year  = {2012},

date = {2012-08-01},

journal = {International Journal of Computational Intelligence Systems},

volume = {5},

number = {4},

pages = {762-774},

abstract = {This paper proposes a parameterized definition for fuzzy comparators on complex fuzzy datatypes like fuzzy collections with conjunctive semantics and fuzzy objects. This definition and its implementation on a Fuzzy Object-Relational Database Management System (FORDBMS) provides the designer with a powerful tool to adapt the behavior of these operators to the semantics of the considered application.},

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@article{Medina2012b,

title = {On the Use of a Fuzzy Object-Relational Database for Flexible Retrieval of Medical Images},

author = {J. M. Medina and S. J. Castillo and C. D. Barranco and J. R. Campaña},

doi = {10.1109/TFUZZ.2012.2201726},

issn = {1941-0034},

year  = {2012},

date = {2012-08-01},

journal = {IEEE Transactions on Fuzzy Systems},

volume = {20},

number = {4},

pages = {786-803},

abstract = {This paper introduces a novel approach to medical image retrieval using a fuzzy object-relational database management system (FORDBMS). The system stores medical images along with information about the content of the image, such as the presence or absence of certain indicators of pathologies. It allows us to flexibly retrieve them on the basis of these indicators, making it possible to obtain images from patients with similar diagnosis and thus, following a common visual pattern. To illustrate the capabilities of the FORDBMS, this paper focuses on X-ray images of patients suffering from scoliosis (a medical condition in which the patient's spine is curved) from which spine descriptions are obtained. Then queries are performed to obtain a set of images with a certain curvature pattern. Results show high accuracy when evaluated by medical experts. Compared with other ad hoc content-based image retrieval systems, the one presented here is easily adaptable to other application domains, customizable, and very scalable.},

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@article{Barranco2012,

title = {An impact ordering approach for indexing fuzzy sets},

author = {C. D. Barranco and S. Helmer},

url = {http://www.sciencedirect.com/science/article/pii/S0165011411000558},

doi = {10.1016/j.fss.2011.01.014},

issn = {0165-0114},

year  = {2012},

date = {2012-06-01},

journal = {Fuzzy Sets and Systems},

volume = {196},

pages = {33-46},

abstract = {We propose an approach for indexing fuzzy data based on inverted files that speeds up retrieval considerably by stopping the traversal of postings lists early. This is possible because the entries in the postings lists are organized in a way that guarantees that there are no matching items beyond a certain point in a list. Consequently, we can reduce the number of false positives significantly, leading to an increase in retrieval performance. We have implemented our approach and evaluated it experimentally, including a test on skewed and real-world data, comparing it to an approach that has previously been shown to be superior to other methods.},

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@article{Gómez-Vela2011b,

title = {Pattern Recognition in Biological Time Series},

author = {F. Gómez-Vela and F. Martínez-Álvarez and C. D. Barranco and N. Díaz-Díaz and D. Rodríguez-Baena and J. Aguilar-Ruiz},

doi = {10.1007/978-3-642-25274-7_17},

isbn = {978-3-642-25274-7},

year  = {2011},

date = {2011-11-01},

journal = {Advances in Artificial Intelligence},

pages = {164-172},

abstract = {Knowledge extraction from gene expression data has been one of the main challenges in the bioinformatics field during the last few years. In this context, a particular kind of data, data retrieved in a temporal basis (also known as time series), provide information about the way a gene can be expressed during time. This work presents an exhaustive analysis of last proposals in this area, particularly focusing on those proposals using non--supervised machine learning techniques (i.e. clustering, biclustering and regulatory networks) to find relevant patterns in gene expression.},

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tppubtype = {article}

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@article{Barranco2009,

title = {Indexing Fuzzy numerical Data with a B+ Tree for Fast Retrieval Using Necessity-Measured Flexible conditions.},

author = {C. D. Barranco and J. R. Campaña and J. M. Medina},

url = {https://doi.org/10.1142/S0218488509006005},

doi = {10.1142/S0218488509006005},

year  = {2009},

date = {2009-11-01},

journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},

volume = {17},

pages = {1-23},

abstract = {This paper proposes an indexing procedure for improving the performance of query processing on a fuzzy database. It focuses on the case when a necessity-measured atomic flexible condition is imposed on the values of a fuzzy numerical attribute. The proposal is to apply a classical indexing structure for numerical crisp data, a B+-tree combined with a Hilbert curve. The use of such a common indexing technique makes its incorporation into current systems straightforward. The efficiency of the proposal is compared with that of another indexing procedure for similar fuzzy data and flexible query types. Experimental results reveal that the performance of the proposed method is similar and more stable than that of its competitor.},

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@article{Barranco2008,

title = {A B+-tree based indexing technique for fuzzy numerical data},

author = {C. D. Barranco and J. R. Campaña and J. M. Medina},

url = {http://www.sciencedirect.com/science/article/pii/S0165011408000274},

doi = {10.1016/j.fss.2008.01.006},

issn = {0165-0114},

year  = {2008},

date = {2008-05-01},

journal = {Fuzzy Sets and Systems},

volume = {159},

number = {12},

pages = {1431-1449},

abstract = {This paper proposes an indexing technique for fuzzy numerical data which increases the performance of query processing when the query involves an atomic possibility measured flexible condition. The proposal is based on a classical indexing mechanism for numerical crisp data, B-tree, which is implemented in most commercial database management systems (DBMS). This makes the proposed technique a good candidate for integration in a fuzzy DBMS when it is developed as an extension of a crisp DBMS. The efficiency of the proposal is contrasted with another indexing method for similar data and queries, G-tree, which is specifically designed to index multidimensional data. Results show that the proposal performance is similar to and more stable than the measured for G-tree when used for indexing fuzzy numbers.},

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@article{Chamorro-Martínez2007,

title = {Retrieving images in fuzzy object-relational databases using dominant color descriptors},

author = {J. Chamorro-Martínez and J. M. Medina and C. D. Barranco and E. Galán-Perales and J. M. Soto-Hidalgo},

url = {http://www.sciencedirect.com/science/article/pii/S0165011406004209},

doi = {10.1016/j.fss.2006.10.013},

issn = {0165-0114},

year  = {2007},

date = {2007-11-01},

journal = {Fuzzy Sets and Systems},

volume = {158},

number = {3},

pages = {312-324},

abstract = {In this paper a fuzzy approach for image retrieval on the basis of color features is presented. The proposal deals with vagueness in the color description and introduces the use of fuzzy database models to store and retrieve imprecise data. To face the color description, the concept of dominant fuzzy color is proposed, using linguistic labels for representing the color information in terms of hue, saturation and intensity. To deal with fuzzy data in our database model, we use a general approach which can support the manipulation of fuzzy objects in an object-relational database system. This allows the retrieval of images by performing flexible queries on the database.},

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