955 resultados para Clasificación Decimal Universal


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Within general characteristics of low-energy few-body systems, we revise some well-known correlations found in nuclear physics, and the properties of low-mass halo nuclei in a three-body neutron-neutron-core model. In this context, near the critical conditions for the occurrence of an Efimov state, we report some results obtained for the neutron- 19C elastic scattering. © 2010 American Institute of Physics.

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This paper proposes a simple and powerful architecture for publication and universal access to smart transducers, through existing and established open standards. Smart transducers are put to work on standards and styles already included in the Web, exploring resources in Cloud Computing and simplifying access to data. © 2012 IEEE.

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Tiene por objetivo facilitar al personal de las unidades de información de la Red IPALCA la realización de funciones utilizando el Mini-Micro CDS/ISIS.

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Incluye Bibliografía

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Incluye Bibliografía

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Protein-protein interactions (PPIs) are essential for understanding the function of biological systems and have been characterized using a vast array of experimental techniques. These techniques detect only a small proportion of all PPIs and are labor intensive and time consuming. Therefore, the development of computational methods capable of predicting PPIs accelerates the pace of discovery of new interactions. This paper reports a machine learning-based prediction model, the Universal In Silico Predictor of Protein-Protein Interactions (UNISPPI), which is a decision tree model that can reliably predict PPIs for all species (including proteins from parasite-host associations) using only 20 combinations of amino acids frequencies from interacting and non-interacting proteins as learning features. UNISPPI was able to correctly classify 79.4% and 72.6% of experimentally supported interactions and non-interacting protein pairs, respectively, from an independent test set. Moreover, UNISPPI suggests that the frequencies of the amino acids asparagine, cysteine and isoleucine are important features for distinguishing between interacting and non-interacting protein pairs. We envisage that UNISPPI can be a useful tool for prioritizing interactions for experimental validation. © 2013 Valente et al.