volume 101, issue 3, P326-326 2016
DOI: 10.1002/cpt.503
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Abstract: the authors present an approach which combines simulation of large biophysical models with machinelearning methods to classify the torsadegeneic risk of a drug. The approach taken by the authors can be viewed as quite complex. They first simulate compounds through three large biophysical models that consist of 10 of differential equations and 100 of parameters. Next, from those simulations 331 metrics were derived for each compound creating a large simulated dataset. The dimensionality of this dataset is then…

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