Evaluation of categorical matrix completion algorithms: toward improved active learning for drug discovery

MOTIVATION: High throughput and high content screening are extensively used to determine the effect of small molecule compounds and other potential therapeutics upon particular targets as part of the early drug development process. However, screening is typically used to find compounds that have a desired effect but not to identify potential undesirable side effects. This is because the size of the search space precludes measuring the potential effect of all compounds on all targets. Active machine learning has been proposed as a solution to this problem. RESULTS: In this article, we describe an improved imputation method, Impute by Committee, for completion of matrices containing categorical values. We compare this method to existing approaches in the context of modeling the effects of many compounds on many targets using latent similarities between compounds and conditions. We also compare these methods for the task of driving active learning in well-characterized settings for synthetic and real datasets. Our new approach performed the best overall both in the accuracy of matrix completion itself and in the number of experiments needed to train an accurate predictive model compared to random selection of experiments. We further improved upon the performance of our new method by developing an adaptive switching strategy for active learning that iteratively chooses between different matrix completion methods. AVAILABILITY AND IMPLEMENTATION: A Reproducible Research Archive containing all data and code is available at http://murphylab.cbd.cmu.edu/software. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.