Alejandro Rodríguez González
Technical University of Madrid
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Featured researches published by Alejandro Rodríguez González.
Journal of Biomedical Semantics | 2014
Alejandro Rodríguez González; Alison Callahan; José Cruz-Toledo; Adrián Jesús García; Mikel Egaña Aranguren; Michel Dumontier; Mark D. Wilkinson
BackgroundTwo distinct trends are emerging with respect to how data is shared, collected, and analyzed within the bioinformatics community. First, Linked Data, exposed as SPARQL endpoints, promises to make data easier to collect and integrate by moving towards the harmonization of data syntax, descriptive vocabularies, and identifiers, as well as providing a standardized mechanism for data access. Second, Web Services, often linked together into workflows, normalize data access and create transparent, reproducible scientific methodologies that can, in principle, be re-used and customized to suit new scientific questions. Constructing queries that traverse semantically-rich Linked Data requires substantial expertise, yet traditional RESTful or SOAP Web Services cannot adequately describe the content of a SPARQL endpoint. We propose that content-driven Semantic Web Services can enable facile discovery of Linked Data, independent of their location.ResultsWe use a well-curated Linked Dataset - OpenLifeData - and utilize its descriptive metadata to automatically configure a series of more than 22,000 Semantic Web Services that expose all of its content via the SADI set of design principles. The OpenLifeData SADI services are discoverable via queries to the SHARE registry and easy to integrate into new or existing bioinformatics workflows and analytical pipelines. We demonstrate the utility of this system through comparison of Web Service-mediated data access with traditional SPARQL, and note that this approach not only simplifies data retrieval, but simultaneously provides protection against resource-intensive queries.ConclusionsWe show, through a variety of different clients and examples of varying complexity, that data from the myriad OpenLifeData can be recovered without any need for prior-knowledge of the content or structure of the SPARQL endpoints. We also demonstrate that, via clients such as SHARE, the complexity of federated SPARQL queries is dramatically reduced.
Journal of Biomedical Semantics | 2014
Mikel Egaña Aranguren; Alejandro Rodríguez González; Mark D. Wilkinson
BackgroundIn recent years Galaxy has become a popular workflow management system in bioinformatics, due to its ease of installation, use and extension. The availability of Semantic Web-oriented tools in Galaxy, however, is limited. This is also the case for Semantic Web Services such as those provided by the SADI project, i.e. services that consume and produce RDF. Here we present SADI-Galaxy, a tool generator that deploys selected SADI Services as typical Galaxy tools.ResultsSADI-Galaxy is a Galaxy tool generator: through SADI-Galaxy, any SADI-compliant service becomes a Galaxy tool that can participate in other out-standing features of Galaxy such as data storage, history, workflow creation, and publication. Galaxy can also be used to execute and combine SADI services as it does with other Galaxy tools. Finally, we have semi-automated the packing and unpacking of data into RDF such that other Galaxy tools can easily be combined with SADI services, plugging the rich SADI Semantic Web Service environment into the popular Galaxy ecosystem.ConclusionsSADI-Galaxy bridges the gap between Galaxy, an easy to use but “static” workflow system with a wide user-base, and SADI, a sophisticated, semantic, discovery-based framework for Web Services, thus benefiting both user communities.
Journal of Biomedical Semantics | 2013
Mikel Egaña Aranguren; Jesualdo Tomás Fernández-Breis; Christopher J. Mungall; Erick Antezana; Alejandro Rodríguez González; Mark D. Wilkinson
BackgroundBiomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment.ResultsWe present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies.ConclusionsCoupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses.
International Journal of Metadata, Semantics and Ontologies | 2012
Alejandro Rodríguez González; Jose María Álvarez Rodríguez; Cristina Casado-Lumbreras; Ricardo Colomo-Palacios
Psychological diagnosis is the process by which mental health professionals determine if problems that affect a person meet all the specific criteria for a psychological disorder. In recent years, decision support systems (DSS) have helped practitioners in the field of psychological clinical diagnosis with notable results. Given that ontologies are created, among other goals, to allow different sorts of formal reasoning, they are seen as valid artefacts to support a new generation of DSS for mental health professionals. Although some initiatives have emerged in order to build realistic ontologies of mental diseases, this field presents a remarkable heterogeneity of data and two different clinical classification systems, which is why this paper presents an ontology with the aim of reusing existing works and serving as the key element of a clinical DSS in the field of mental disorders.
bioRxiv | 2018
Eduardo P. Garcia del Valle; Gerardo Lagunes Garcia; Lucia Prieto Santamaria; Massimiliano Zanin; Ernestina Menasalvas Ruiz; Alejandro Rodríguez González
Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks.
bioRxiv | 2018
Gerardo Lagunes Garcia; Alejandro Rodríguez González; Lucia Prieto Santamaria; Eduardo P. Garcia del Valle; Massimiliano Zanin; Ernestina Menasalvas Ruiz
Abstract Within the global endeavour of improving population health, one major challenge is the increasingly high cost associated with drug development. Drug repositioning, i.e. finding new uses for existing drugs, is a promising alternative; yet, its effectiveness has hitherto been hindered by our limited knowledge about diseases and their relationships. In this paper, we present DISNET (disnet.ctb.upm.es), a web-based system designed to extract knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia, PubMed and Mayo Clinic; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. We further present a validation of the processing performed by the system; and describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses.Within the global endeavour of improving population health, one major challenge is the increasingly high cost associated with drug development. Drug repositioning, i.e. finding new uses for existing drugs, is a promising alternative; yet, its effectiveness has hitherto been hindered by our limited knowledge about diseases and their relationships. In this paper we present DISNET (Drug repositioning and disease understanding through complex networks creation and analysis), a web-based system designed to extract knowledge from signs and symptoms retrieved from medical data bases, and to enable the creation of customisable disease networks. We here present the main functionalities of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia, PubMed and MayoClinic; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. We further present a validation of the processing performed by the system; and describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. Database URL: http://disnet.ctb.upm.es
11th International Conference on Practical Applications of Computational Biology & Bioinformatics, 2017, ISBN 978-3-319-60815-0, págs. 137-145 | 2017
César Antonio Ortiz Toro; Consuelo Gonzalo Martín; Ángel Mario García Pedrero; Alejandro Rodríguez González; Ernestina Menasalvas
Determining the severity and potential aggressiveness of breast cancer is an important step in the determination of the treatment options for a patient. Mitosis activity is one of the main components in breast cancer severity grading. Currently, mitosis counting is a laborious, prone to processing errors, done manually by a pathologist.
user centric media | 2009
Alejandro Rodríguez González; Ana Cerezo; David Jiménez; José Manuel Menéndez
This paper aims to detail an innovative multimedia edition system. A special functionality provides this solution with different ways of managing audiovisual sources. It has been specially designed for media centralized environments dealing with large files and groups of users that access contents simultaneously for editing and composing. Mass media headquarters or user communities can take advantage of the two working modes, which allow online and offline workflows. The application provides a user edition interface, audiovisual processing and encoding based on GPL tools, communication via SOAP between client and server, independent and portable edition capabilities and easy adaptable source handling system for different technologies.
Archive | 2013
Jose María Álvarez Rodríguez; Luis Polo Paredes; Emilio Rubiera Azcona; Alejandro Rodríguez González; José Emilio Labra Gayo; Patricia Ordóñez de Pablos
IWBBIO | 2013
Alejandro Rodriguez Iglesias; Mikel Egaña Aranguren; Alejandro Rodríguez González; Mark Wilkinson