Improving Drug Combination Repositioning using Positive Unlabelled Learning and Ensemble Learning

Abstract

Drug repositioning is a cost-effective and timeeffective concept that enables the use of existing drugs/drug combinations for therapeutic effects. The number of drug combinations used for therapeutic effects is smaller than all possible drug combinations in the present drug databases. These databases consist of a smaller set of labelled positives and a majority of unlabelled drug combinations. Therefore, there is a need for determining both reliable positive and reliable negative samples to develop binary classification models. Since, we only have labelled positives, the unlabelled data has to be separated into positives and negatives by a reliable technique. This study proposes and demonstrates the significance of using Positive Unlabelled Learning, for determining reliable positive and negative drug combinations for drug repositioning. In the proposed approach, the dataset with known positives and unlabelled samples was clustered by a Deep Learning based Self Organizing Map. Then, an ensemble learning methodology was followed by employing three classification models. The proposed PUL model was compared with the frequently used approach that randomly selects negative drug pairs from unlabelled samples. A significant improvement of 19.15%, 20.56% and 20.23% in the Precision, Recall and F-measure, respectively, was observed for the proposed PUL-based ensemble learning approach. Moreover, 128 drug repositioning candidates were predicted by the proposed methodology. Further, we found literature-based evidence to support five drug combinations that may be able to be repositioned. These discoveries show our proposed PUL approach as a promising strategy that is applicable in drug combination prediction for repositioning.