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  • Authors: Le, Thi Thu Huong (2015)

  • In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbal-anced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, var-ious strategies including cost-sensitive learning and resam-plingmethodswere studied to tackle themoderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were con-structed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performanc...