Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handli...

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Bibliographic Details
Main Authors: Gabriel Idakwo, Sundar Thangapandian, Joseph Luttrell, Yan Li, Nan Wang, Zhaoxian Zhou, Huixiao Hong, Bei Yang, Chaoyang Zhang, Ping Gong
Format: Article
Language:English
Published: BMC 2020-10-01
Series:Journal of Cheminformatics
Subjects:
Structure–activity relationship (SAR)
Chemical classification
Molecular fingerprints
Random forest (RF)
Ensemble learning
Bootstrap aggregation (bagging)
Online Access:http://link.springer.com/article/10.1186/s13321-020-00468-x

Internet

http://link.springer.com/article/10.1186/s13321-020-00468-x

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