Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling
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 performance than the
cost-sensitive classification models, especially in the case of
oversampling data wheremisclassification rates for minority
class have values of 0.11 and 0.14 for training and test set,
respectively. Aconsensusmodel with enhanced applicability
domain was subsequently constructed and showed improved
performance. This model was used to predict a set of ran-domly selected
high-permeability reference drugs according
to the biopharmaceutics classification system. Overall, this
study provides a comparison of numerous rebalancing strate-gies and
displays the effectiveness of oversampling methods
to deal with imbalanced permeability data problems
http://repository.vnu.edu.vn/handle/VNU_123/11505
http://repository.vnu.edu.vn/handle/VNU_123/11505
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