Classifier Ensemble Based on Feature Selection and Diversity Measures for Predicting the Affinity of A2B Adenosine Receptor Antagonists

• Published in: Journal of chemical information and modeling Vol. 53; no. 12; pp. 3140 - 3155
• Main Authors: Bonet, Isis, Franco-Montero, Pedro, Rivero, Virginia, Teijeira, Marta, Borges, Fernanda, Uriarte, Eugenio, Morales Helguera, Aliuska
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 23.12.2013
• Subjects: Applied sciences; Biological and medical sciences; Computer science; control theory; systems; Data processing. List processing. Character string processing; Diabetes. Impaired glucose tolerance; Endocrine pancreas. Apud cells (diseases); Endocrinopathies; Exact sciences and technology; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Memory organisation. Data processing; Ophthalmology; Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Software; Tumors; Endocrinopathy; Multimodel; Nearest neighbour; Retinopathy; Diabetes mellitus; Metabolic diseases; Graph theory; Glucose; Neural network; Malignant tumor; Decision tree; Aggregate model; Mathematical function; Cancerology; Vector support machine; Selection criterion; Antagonist; Adenosine receptor; Learning algorithm; Artificial intelligence; Multiple classification; Cancer
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci300516w

A2B adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A2B adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A2B adenosine receptor antagonists, and it can be used to develop other QSAR models.