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Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 8th International Me Elia Biganzoli,Alfredo Vellido,Roberto Taglia

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Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computatir demonstrates that it is possible to model and trace over time the effect of a gene on the total spiking behavior of the SNN when the gene controls a parameter of a stochastic spiking neuron model used to build the SNN. Such CNGM can be potentially used to study neurodegenerative diseases or develop CNGM for cognitive robotics.
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Boosted C5 Trees i-Biomarkers Panel for Invasive Bladder Cancer Progression Predictionmining. These are used both in feature selection and classifier development. The approach is designed to avoid overfitting and overoptimistic results. To our knowledge, these are the first intelligent systems for prediction of bladder cancer progression, based on boosted C5 decision trees, and their accuracy of 100% is the best published by now.
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Conference proceedings 2012cs and Biostatistics, CIBB 2011, held in Gargnano del Garda, Italy, in June/July 2011. The 19 papers, presented together with 2 keynote speeches, were carefully reviewed and selected from 24 submissions. The papers are organized in topical sections on statistical learning, genomics, computational in
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https://doi.org/10.1007/978-3-322-86275-4d with severe sepsis and multi-organ failure at the ICU of Vall d’Hebrón University Hospital (Barcelona, Spain). Outcome was predicted using statistic algebraic models and the Bayesian Networks that can naturally be derived from these models.
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https://doi.org/10.1007/978-3-322-93058-3ify clusters containing proteins with the same functional category, improving on ., a popular state-of-the-art method for functional protein complex detection. Moreover, application of ., to two popular benchmark networks results in the detection of accurate communities, substantiating the effectiveness of the proposed method in diverse domains.
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https://doi.org/10.1007/978-3-322-93058-3ed new challenges for the design of efficient algorithms able to produce results in reasonable time. In this work we present an updated version of our algorithm Weeder, in which time and space requirements are significantly reduced and, moreover, also the accuracy of the results is notably improved.
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On the Use of Graphical Models to Study ICU Outcome Prediction in Septic Patients Treated with Statid with severe sepsis and multi-organ failure at the ICU of Vall d’Hebrón University Hospital (Barcelona, Spain). Outcome was predicted using statistic algebraic models and the Bayesian Networks that can naturally be derived from these models.
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