Titlebook: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics; 5th European Confere Elena Marchiori,Jason H. Moore,Jagath C.

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y cleaving the virus polypeptides. Many efforts have been devoted to perform accurate predictions on the HIV-protease cleavability of peptides, in order to design efficient inhibitor drugs. Over the last decade, linear and nonlinear supervised learning methods have been extensively used to discrimin

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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics978-3-540-71783-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Identifying Regulatory Sites Using Neighborhood Species,ion model that linearly relates gene expression levels to the matching scores of nucleotide patterns allows us to identify DNA-binding sites from a collection of co-regulated genes and their nearby non-coding DNA sequences. Our methodology uses Bayesian models and stochastic search techniques to sel

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Genetic Programming and Other Machine Learning Approaches to Predict Median Oral Lethal Dose (LD50)velopment. Here we present an empirical study focusing on various versions of Genetic Programming and other well known Machine Learning techniques to predict Median Oral Lethal Dose (LD.) and Plasma Protein Binding (%PPB) levels. Since these two parameters respectively characterize the harmful effec

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Hypothesis Testing with Classifier Systems for Rule-Based Risk Prediction,d missing data must be tolerated, the results should be easily interpretable. Moreover, with genetic data, often the combination of two or more attributes leads to non-linear effects not detectable for each attribute on its own. We present a new ML algorithm, HCS, taking inspiration from learning cl

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