Black Box FDR
Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan

TL;DR
Black Box FDR is a novel empirical-Bayes method that uses deep learning models to improve the detection of significant results and relevant features in large-scale multi-experiment studies, enhancing power and FDR control.
Contribution
It introduces a two-stage black box modeling approach for multi-experiment analysis, leveraging neural networks to boost discovery power and interpretability.
Findings
Outperforms state-of-the-art methods in benchmarks.
Increases discovery of significant outcomes in cancer studies.
Identifies key genomic drivers of drug response.
Abstract
Analyzing large-scale, multi-experiment studies requires scientists to test each experimental outcome for statistical significance and then assess the results as a whole. We present Black Box FDR (BB-FDR), an empirical-Bayes method for analyzing multi-experiment studies when many covariates are gathered per experiment. BB-FDR learns a series of black box predictive models to boost power and control the false discovery rate (FDR) at two stages of study analysis. In Stage 1, it uses a deep neural network prior to report which experiments yielded significant outcomes. In Stage 2, a separate black box model of each covariate is used to select features that have significant predictive power across all experiments. In benchmarks, BB-FDR outperforms competing state-of-the-art methods in both stages of analysis. We apply BB-FDR to two real studies on cancer drug efficacy. For both studies,…
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Taxonomy
TopicsComputational Drug Discovery Methods · Statistical Methods in Clinical Trials · Machine Learning and Data Classification
