Consensus Policies to Solve Bioinformatic Problems - Armañanzas, Rubén
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Disciplines of bioinformatics and computational biology have emerged from the convergence of the new omics fields and the computational tools that are needed to manage, store and analyze the huge amount of data produced by them. One of the most classical problems in computational biology research is to extract knowledge from population studies. This kind of research is mapped by the machine learning discipline into the supervised classification problems. Several models exist to accomplish this task, but, in order to extract useful biological knowledge, the classifiers based on Bayesian…mehr

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Produktbeschreibung
Disciplines of bioinformatics and computational biology have emerged from the convergence of the new omics fields and the computational tools that are needed to manage, store and analyze the huge amount of data produced by them. One of the most classical problems in computational biology research is to extract knowledge from population studies. This kind of research is mapped by the machine learning discipline into the supervised classification problems. Several models exist to accomplish this task, but, in order to extract useful biological knowledge, the classifiers based on Bayesian networks are of the most useful. In optimization, classical search strategies are unfeasible to deal with high-dimensionality biological problems, where the current computer power is still insufficient to provide exhaustive searches. Therefore, machine learning and optimization procedures need accommodation to the specificities of the novel biological data. This book aims to contribute to the state-of-the-art of machine learning techniques adapted for dealing with computational biology problems.
Autorenporträt
Rubén Armañanzas obtained his Ph.D. in 2009 at University of the Basque Country, Spain. His research topics included computational biology, neuroinformatics, evolutionary computation and feature selection. Applications of these techniques were focused on biomarkers discovery in complex diseases or deciphering gene interactions, among others.