Confidence Bands and Hypothesis Test Methods for Recall and Precision Curves at Extremely Small Fractions with Applications to Drug Discovery
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In virtual screening for drug discovery, recall curves are used to assess the performance of ranking algorithms, in which recall is a function of the fraction of data prioritized for experimental testing. Unfortunately, researchers almost never consider the uncertainty in the estimation of the recall curve when benchmarking algorithms. We confirm that a recently developed procedure for estimating pointwise confidence intervals for recall curves – and closely related variants, such as precision curves – can be applied to a variety of simulated data sets representative of those typically encountered in virtual screening. Since it is more desirable in benchmarks to present the uncertainty of performance over a range of testing fractions, we extend the pointwise confidence interval procedure to allow for the estimation of confidence bands for these curves. We also present hypothesis test methods to determine significant differences between the curves for competing algorithms. We show these methods have high power to detect significant differences at a range of small fractions typically tested, while maintaining control of type I error rate. These methods enable statistically rigorous comparisons of virtual screening algorithms using a metric that quantifies the aspect of performance that is of primary interest.
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