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. Author manuscript; available in PMC: 2018 May 30.
Published in final edited form as: J Chem Inf Model. 2018 May 10;58(5):943–956. doi: 10.1021/acs.jcim.7b00641

Figure 1. Diagram illustrating a combined classifier framework for prediction of drug-induced cardiovascular (CV) complications.

Figure 1

Five types of drug-induced CV complications are collected from three public databases (CTD, SIDER and MetaADEDB). The single classifiers are built on the basis of molecular fingerprints and the selected physical descriptors using four machine-learning algorithms (logistic regression, random forest, k-nearest neighbors, and support vector machine). The four best single highest performance classifiers were picked for building the combined classifiers using a neural network algorithm. The performance of all models was evaluated by both 5-fold cross-validation and the external validation sets collected from Offsides database 20. kNN: k-nearest neighbors; SVM: support vector machine; RF: random forest; LR: logistic regression.