Comprehensive evaluation of collaborative filtering in drug repurposing
Résumé
Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery to consider reusing approved compounds to mitigate those issues. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities. To tackle this classification problem, collaborative filtering with implicit feedback has become popular. It can handle large imbalances between negative and positive known associations and known and unknown associations. However, properly evaluating the improvement over the state of the art is challenging, as there is no consensus approach to compare models. We propose a reproducible methodology for comparing collaborative filtering-based drug repurposing. We illustrate this method by comparing 11 models from the literature on eight diverse drug repurposing datasets. Based on this benchmark, we derive guidelines to ensure a fair and comprehensive evaluation of the performance of those models. Those contributions constitute an essential step towards increased reproducibility and more accessible development of competitive drug repurposing methods.
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