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Many clinical studies are greatly dependent on an efficient identification of relevant datasets. This selection can be performed in existing health data catalogues, by searching for available metadata. The search process can be optimised through questioning-answering interfaces, to help researchers explore the available data present. However, when searching the distinct catalogues the lack of metadata harmonisation imposes a few bottlenecks. This paper presents a methodology to allow semantic search over several biomedical database catalogues, by extracting the information using a shared domain knowledge. The resulting pipeline allows the converted data to be published as FAIR endpoints, and it provides an end-user interface that accepts natural language questions.

Over the years, several approaches have tried to tackle the problem of performing an automatic scoring of the sleeping stages. Although any polysomnography usually collects over a dozen of different signals, this particular problem has been mainly tackled by using only the Electroencephalograms presented in those records. On the other hand, the other recorded signals have been mainly ignored by most works. This paper explores and compares the convenience of using additional signals apart from electroencephalograms. More specifically, this work uses the SHHS-1 dataset with 5,804 patients containing an electromyogram recorded simultaneously as two electroencephalograms. To compare the results, first, the same architecture has been evaluated with different input signals and all their possible combinations. These tests show how, using more than one signal especially if they are from different sources, improves the results of the classification. Additionally, the best models obtained for each combination of one or more signals have been used in ensemble models and, its performance has been compared showing the convenience of using these multi-signal models to improve the classification. The best overall model, an ensemble of Depth-wise Separational Convolutional Neural Networks, has achieved an accuracy of 86.06\% with a Cohen's Kappa of 0.80 and a $F_{1}$ of 0.77. Up to date, those are the best results on the complete dataset and it shows a significant improvement in the precision and recall for the most uncommon class in the dataset., 16 pages, 11 tables and 7 figures

This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way in order to address two different problems: music representation into the latent space, and using this representation to make predictions of the future values of the musical piece. This approach was trained with different songs of a classical composer. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions in unseen data., Comment: 9 pages, 4 figures

El framework J2EE ha sido el gran dominador, durante mucho tiempo, en el desarrollo de aplicaciones empresariales. Esto hecho originó la aparición de un rico ecosistema de herramientas, manuales, tutoriales, etc., que explican las diferentes alternativas o peculiaridades a la hora de su implementación. La irrupción de .NET Framework, en el ámbito empresarial, ha producido una fuerte demanda de implementación de aplicaciones bajo dicha arquitectura. Sin embargo, la cantidad o calidad de la documentación disponible dista considerablemente con respecto a la existente, su principal alternativa (J2EE). Esta laguna de documentación, es especialmente visible y preocupante cuando se establece como objetivo dar a conocer los conceptos del framework, desde un punto de vista docente, a los futuros egresados del Grado en Ingeniería en Informática. Este trabajo describe el enfoque docente seguido, para alcanzar el citado objetivo de familiarizar a los alumnos con este framework alternativo y las prácticas habituales de modelo dentro de éste. Para ello, se basa principalmente de un conjunto de sencillos tutoriales con los que mostrar los fundamentos de la tecnología y dos aplicaciones completas (miniportal y minibank) en las que se muestra cómo aplicar patrones de diseño, a la hora de abordar una aplicación empresarial.For a long time, J2EE has been the dominating framework for the development of business applications. This fact resulted in a rich ecosystem of tools, manuals, tutorials, etc. that explain different implementation alternatives or peculiarities. The incursion of .NET Framework in the business environment has generated a strong demand of application implementation under this architecture. However, the quantity and quality of documents available significantly differs from its main alternative (J2EE). This documentation gap is especially visible and worrying when the objective is to teach the concepts of Framework, from a teacher’s point of view, to the future graduates of the Information Systems Engineering program. This paper describes the teaching approach used in order to achieve the goal of having the students become familiar with this alternative framework and the usual model practices within it. Thus, it is based mainly on a set of basic tutorials that show the foundations of technology and two complete applications (miniportal and minibank) explaining how to apply design patterns when developing a business solution.EDITORIAL: UniversidadPeruana de Ciencias Aplicadas (UPC)REVISTA: Revista Digital deInvestigación en Docencia Universitaria (RIDU)FECHA DE PUBLICACIÓN: jun-2015URL: http://hdl.handle.net/10757/604897ENLACES ADICIONALES:http://revistas.upc.edu.pe/index.php/docencia/article/view/385PDF: Full text-Texto completo

El framework J2EE ha sido el gran dominador, durante mucho tiempo, en el desarrollo de aplicaciones empresariales. Esto hecho originó la aparición de un rico ecosistema de herramientas, manuales, tutoriales, etc., que explican las diferentes alternativas o peculiaridades a la hora de su implementación. La irrupción de .NET Framework, en el ámbito empresarial, ha producido una fuerte demanda de implementación de aplicaciones bajo dicha arquitectura. Sin embargo, la cantidad o calidad de la documentación disponible dista considerablemente con respecto a la existente, su principal alternativa (J2EE). Esta laguna de documentación, es especialmente visible y preocupante cuando se establece como objetivo dar a conocer los conceptos del framework, desde un punto de vista docente, a los futuros egresados del Grado en Ingeniería en Informática. Este trabajo describe el enfoque docente seguido, para alcanzar el citado objetivo de familiarizar a los alumnos con este framework alternativo y las prácticas habituales de modelo dentro de éste. Para ello, se basa principalmente de un conjunto de sencillos tutoriales con los que mostrar los fundamentos de la tecnología y dos aplicaciones completas (miniportal y minibank) en las que se muestra cómo aplicar patrones de diseño, a la hora de abordar una aplicación empresarial. For a long time, J2EE has been the dominating framework for the development of business applications. This fact resulted in a rich ecosystem of tools, manuals, tutorials, etc. that explain different implementation alternatives or peculiarities. The incursion of .NET Framework in the business environment has generated a strong demand of application implementation under this architecture. However, the quantity and quality of documents available significantly differs from its main alternative (J2EE). This documentation gap is especially visible and worrying when the objective is to teach the concepts of Framework, from a teacher’s point of view, to the future graduates of the Information Systems Engineering program. This paper describes the teaching approach used in order to achieve the goal of having the students become familiar with this alternative framework and the usual model practices within it. Thus, it is based mainly on a set of basic tutorials that show the foundations of technology and two complete applications (miniportal and minibank) explaining how to apply design patterns when developing a business solution. AUTOR(ES):Gesta, Marcos;Vázquez, José M.;Fernández-Blanco,Enrique;Rivero, Daniel;Rabuñal, Juan R.;Dorado, Julian;Pazos, Alejandro EDITORIAL:UniversidadPeruana de Ciencias Aplicadas (UPC) REVISTA:Revista Digital deInvestigación en Docencia Universitaria (RIDU) FECHA DE PUBLICACIÓN:jun-2015 URL:http://hdl.handle.net/10757/604897 ENLACES ADICIONALES:http://revistas.upc.edu.pe/index.php/docencia/article/view/385

Series Studies in Health Technology and Informatics Ebook Volume 192: MEDINFO 2013 The development of personalized medicine is tightly linked with the correct exploitation of molecular data, especially those associated with the genome sequence along with these use of genomic data there is an increasing demand to share these data for research purposes. Transition of clinical data to research is based in the anonymization of these data so the patient cannot be identified, the use of genomic data poses a great challenge because its nature of identifying data. In this work we have analyzed current methods for genome anonymization and propose a one way encryption method that may enable the process of genomic data sharing accessing only to certain regions of genomes for research purposes. Sí

The development of Artificial Neural Networks (ANNs) is usually a slow process in which the human expert has to test several architectures until he finds the one that achieves best results to solve a certain problem. This work presents a new technique that uses Genetic Programming (GP) for automatically generating ANNs. To do this, the GP algorithm had to be changed in order to work with graph structures, so ANNs can be developed. This technique also allows the obtaining of simplified networks that solve the problem with a small group of neurons. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases. The results of those comparisons show that the system achieves good results comparable with the already existing techniques and, in most of the cases, they worked better than those techniques., {"references":["S. Haykin, Neural Networks (2nd ed.), Englewood Cliffs, NJ: Prentice\nHall, 1999.","J. R. Rabu├▒al and J. Dorado, (eds.) Artificial Neural Networks in Real-\nLife Applications, Idea Group Inc, 2005.","J. R. Koza, Genetic Programming: On the Programming of Computers\nby Means of Natural Selection, Cambridge, MA, MIT Press, 1992.","J.R. Rabu├▒al, J. Dorado, A. Pazos, J. Pereira and D. Rivero, \"A New\nApproach to the Extraction of ANN Rules and to Their Generalization\nCapacity Through GP\". Neural Computation, vol. 16, n. 7. 2004. pp.\n1483-1523.","M. Bot, \"Application of Genetic Programming to Induction of Linear\nClassification Trees\", Final Term Project Report, Vrije Universiteit,\nAmsterdam, 1999.","J. R. Rabu├▒al, J. Dorado, J. Puertas, A. Pazos, A. Santos and D. Rivero,\n\"Prediction and Modelling of the Rainfall-Runoff Transformation of a\nTypical Urban Basin using ANN and GP\", Applied Artificial\nIntelligence, 2003.","R. S. Sutton, \"Two problems with backpropagation and other steepestdescent\nlearning procedure for networks\", Proc. 8th Annual Conf.\nCognitive Science Society, Hillsdale, NJ: Erlbaum, 1986, pp. 823-831.","D. J. Janson and J. F. Frenzel, \"Training product unit neural networks\nwith genetic algorithms\", IEEE Expert, vol. 8, 1993, pp. 26-33.","G. W. Greenwood, \"Training partially recurrent neural networks using\nevolutionary strategies\", IEEE Trans. Speech Audio Processing, vol. 5,\n1997, pp. 192-194.\n[10] E. Alba, J. F. Aldana and J. M. Troya, \"Fully automatic ANN design: A\ngenetic approach\", Proc. Int. Workshop Artificial Neural Networks\n(IWANN-93), Lecture Notes in Computer Science, vol. 686. Berlin,\nGermany: Springer-Verlag, 1993, pp. 399-404.\n[11] H. Kitano, \"Designing neural networks using genetic algorithms with\ngraph generation system\", Complex Systems, vol. 4, 1990, pp. 461-476.\n[12] X. Yao and Y. Liu, \"Toward designing artificial neural networks by\nevolution\", Appl. Math. Computation, vol. 91, no. 1, 1998, pp. 83-90.\n[13] S. A. Harp, T. Samad and A. Guha, \"Toward the genetic synthesis of\nneural networks\", Proc. 3rd Int. Conf. Genetic Algorithms and Their\nApplications, J.D. Schafer, Ed. San Mateo, CA: Morgan Kaufmann,\n1989, pp. 360-369.\n[14] S. Nolfi and D. Parisi, \"Evolution of Artificial Neural Networks\",\nHandbook of brain theory and neural networks, Second Edition,\nCambridge, MA: MIT Press, 2002, pp. 418-421.\n[15] P. Turney, D. Whitley and R. Anderson, \"Special issue on the\nbaldwinian effect\", Evolutionary Computation, vol. 4, no. 3, 1996, pp.\n213-329.\n[16] A. Zomorodian, 1995. \"Context-free Language Induction by Evolution\nof Deterministic Push-down Automata Using Genetic Programming\", in\nWorking Notes of the Genetic Programming Symposium, AAAI-95, Eric\nSiegel and John Koza, chairs. AAAI Press. 1995.\n[17] Z. Fan, K. Seo, R. C. Rosenberg, J. Hu and E. D. Goodman, \"Exploring\nMultiple Design Topologies Using Genetic Programming And Bond\nGraphs\". GECCO 2002: Proceedings of the Genetic and Evolutionary\nComputation Conference. Springer-Verlag. 2002, pp. 1073-1080\n[18] Z. Fan, K. Seo, J. Hu, R. C. Rosenberg and E. D. Goodman, \"System-\nLevel Synthesis of MEMS via Genetic Programming and Bond Graphs\",\nGenetic and Evolutionary Computation -- GECCO-2003. Vol. 2724.\n2003, pp. 2058-2071.\n[19] F. Gruau, \"Genetic micro programming of neural networks\", in Kinnear,\nJr., K. E., editor, Advances in Genetic Programming, chapter 24, MIT\nPress, 1994, pp. 495-518.\n[20] S. Luke and L. Spector, \"Evolving Graphs and Networks with Edge\nencoding: Preliminary Report\". In Late Breaking Papers at the Genetic\nProgramming 1996 Conference (GP96). J. Koza, ed. Stanford: Stanford\nBookstore, 1996, pp. 117-124.\n[21] A. Teller, \"Evolving Programmers: The Co-evolution of Intelligent\nRecombination Operators\", in Advances in Genetic Programming II, P.\nAngeline and K. Kinnear, editors. Cambridge: MIT Press., 1996.\n[22] W. Kantschik, P. Dittrich, M. Brameier and W. Banzhaf,\n\"MetaEvolution in Graph GP\", Proceedings of EuroGP'99, LNCS, Vol.\n1598. SpringerVerlag, 1999, pp. 15-28.\n[23] R. Poli \"Evolution of Graph-like Programs with Parallel Distributed\nGenetic Programming\", Genetic Algorithms: Proceedings of the Seventh\nInternational Conference, 1997.\n[24] W. Kantschik, W. Banzhaf, \"Linear-Graph GP - A new GP Structure\",\nin Proceedings of the 4th European Conference on Genetic\nProgramming, EuroGP 2002, 2002.\n[25] A. Teller A. and M. Veloso, \"Internal reinforcement in a connectionist\ngenetic programming approach\", Artificial Intelligence. Vol. 120, N. 2,\n2000, pp. 165-198.\n[26] D. J. Montana, \"Strongly typed genetic programming\", Evolutionary\nComputation, Vol. 3, No. 2, 1995, pp. 199-200.\n[27] C. J. Mertz and P. M. Murphy, UCI repository of machine learning\ndatabases. http://www-old.ics.uci.edu/pub/machine-learning-databases,\n2002\n[28] E. Cant├║-Paz and C. Kamath, \"An Empirical Comparison of\nCombinations of Evolutionary Algorithms and Neural Networks for\nClassification Problems\", IEEE Transactions on systems, Man and\nCybernetics - Part B: Cybernetics, 2005, pp. 915-927.\n[29] T. G. Dietterich, \"Approximate statistical tests for comparing supervised\nclassification learning algorithms\", Neural Computation, Vol. 10, No. 7,\n1998, pp. 1895-1924."]}

Background. Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. Methods. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Results. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Conclusions. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender, and via a Web service API).

Ontology Recommender traffic summary. Summary of traffic received by the Ontology Recommender for the period 2014–2016, compared to the other most used BioPortal services. (PDF 27 kb)

Ontology Recommender traffic summary. Summary of traffic received by the Ontology Recommender for the period 2014–2016, compared to the other most used BioPortal services. (PDF 27 kb)