Developing reliable classification of dual IDO1/TDO inhibitors using data fusion and majority voting

Abstract

Indoleamine 2,3-dioxygenase 1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) are promising dual-targeting inhibitors in cancer and neurodegenerative diseases treatment. Data fusion of receptor-based and ligand-based information of dual IDO1/TDO inhibitors were employed for active/inactive classification performance. A reliable decision making procedure was used here to identify active/inactive dual IDO1/TDO inhibitors using majority voting method and pools of individual classifications instead of individual models. All classification models were validated using prediction set, cross-validation and y-scrambling tests. The classification outcomes indicate that the sensitivity, specificity, precision, accuracy, G-mean and F1 score values increases up to ∼90% using data fusion and majority voting method. Compare to individual classification models with a single prediction point, the majority voting method has more reliable results due to the integration of the pool of individual classification models. This classification strategy may lead to more reliable identification of active/inactive dual-targeting inhibitors in cancer immunotherapy. Communicated by Ramaswamy H. Sarma