# Black Box FDR

arXiv: https://arxiv.org/abs/1806.03143 · HTML: https://mlsanity.com/paper/1806.03143/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 6 of 6)

- [NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy](https://mlsanity.com/paper/2101.09809/) (2021)
- [DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data](https://mlsanity.com/paper/2310.13349/) (2023)
- [Treatment response as a latent variable](https://mlsanity.com/paper/2502.08776/) (2025)
- [AdaPT-GMM: Powerful and robust covariate-assisted multiple testing](https://mlsanity.com/paper/2106.15812/) (2021)
- [Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning](https://mlsanity.com/paper/2209.07011/) (2022)
- [Probabilistic Model Incorporating Auxiliary Covariates to Control FDR](https://mlsanity.com/paper/2210.03178/) (2022)

## Abstract (arXiv)

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, BB-FDR increases the proportion of significant outcomes discovered and selects variables that reveal key genomic drivers of drug sensitivity and resistance in cancer.