Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 10th European Confer Mario Giacobini,Leonardo Vanneschi,Willi

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Inferring Disease-Related Metabolite Dependencies with a Bayesian Optimization Algorithmng the classification accuracy of otherwise hardly classifiable metabolomics data and that the detected metabolite dependencies can be mapped to physiological pathways, which in turn were affirmed by literature from the domain.

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A GPU-Based Multi-swarm PSO Method for Parameter Estimation in Stochastic Biological Systems Exploitly sampled. The whole method has been specifically designed and entirely executed on the GPU to provide a reduction of computational costs. We show the effectiveness of our method and discuss its performances on an enzymatic kinetics and a prokaryotic gene expression network.

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Comparing Multiobjective Artificial Bee Colony Adaptations for Discovering DNA Motifsor this purpose, we have used the Artificial Bee Colony algorithm, a novel Swarm Intelligence algorithm based on the intelligent behavior of honey bees. As we will see, the use of one or another multiobjective adaptation causes significant differences in the results.

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Inferring Phylogenetic Trees Using a Multiobjective Artificial Bee Colony Algorithmy, to reconstruct phylogenetic trees according to two criteria: maximum parsimony and maximum likelihood. Our approach shows a significant improvement in the quality of the inferred trees compared to other multiobjective proposals.

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978-3-642-29065-7Springer-Verlag Berlin Heidelberg 2012

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics978-3-642-29066-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Mario Giacobini,Leonardo Vanneschi,William S. BushFast track conference proceedings.Unique visibility.State of the art research

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