# Variable selection for BART: An application to gene regulation

arXiv: https://arxiv.org/abs/1310.4887 · HTML: https://mlsanity.com/paper/1310.4887/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2013.

## Built on by (most cited first, 10 of 10)

- [Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration](https://mlsanity.com/paper/2110.00653/) (2021)
- [Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large p](https://mlsanity.com/paper/2410.13681/) (2024)
- [Variable importance scores](https://mlsanity.com/paper/2102.07765/) (2021)
- [Bayesian CART models for insurance claims frequency](https://mlsanity.com/paper/2303.01923/) (2023)
- [Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection](https://mlsanity.com/paper/2410.02623/) (2024)
- [Variable selection with missing data in both covariates and outcomes: Imputation and machine learning](https://mlsanity.com/paper/2104.02769/) (2021)
- [Extraction of Visual Information to Predict Crowdfunding Success](https://mlsanity.com/paper/2203.14806/) (2022)
- [Generalized Bayesian Additive Regression Trees: Theory and Software](https://mlsanity.com/paper/2304.12505/) (2023)
- [Co-data Learning for Bayesian Additive Regression Trees](https://mlsanity.com/paper/2311.09997/) (2023)
- [Semi-Parametric Bayesian Additive Regression Trees for Risk Prediction with High-Dimensional Epigenetic Signatures and Low-Dimensional Covariates](https://mlsanity.com/paper/2605.20143/) (2026)

## Abstract (arXiv)

We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266-298] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine.