# Time-to-event prediction for grouped variables using Exclusive Lasso

arXiv: https://arxiv.org/abs/2504.01520 · HTML: https://mlsanity.com/paper/2504.01520/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Large-scale benchmark study of survival prediction methods using multi-omics data](https://mlsanity.com/paper/2003.03621/) (2020)
- [Within Group Variable Selection through the Exclusive Lasso](https://mlsanity.com/paper/1505.07517/) (2015)
- [Correlated Feature Selection with Extended Exclusive Group Lasso](https://mlsanity.com/paper/2002.12460/) (2020)
- [Exclusive Group Lasso for Structured Variable Selection](https://mlsanity.com/paper/2108.10284/) (2021)
- [Exclusive Sparsity Norm Minimization with Random Groups via Cone Projection](https://mlsanity.com/paper/1510.07925/) (2015)
- [Adaptive Sieving with PPDNA: Generating Solution Paths of Exclusive Lasso Models](https://mlsanity.com/paper/2009.08719/) (2020)

## Abstract (arXiv)

The integration of high-dimensional genomic data and clinical data into time-to-event prediction models has gained significant attention due to the growing availability of these datasets. Traditionally, a Cox regression model is employed, concatenating various covariate types linearly. Given that much of the data may be redundant or irrelevant, feature selection through penalization is often desirable. A notable characteristic of these datasets is their organization into blocks of distinct data types, such as methylation and clinical predictors, which requires selecting a subset of covariates from each group due to high intra-group correlations. For this reason, we propose utilizing Exclusive Lasso regularization in place of standard Lasso penalization. We apply our methodology to a real-life cancer dataset, demonstrating enhanced survival prediction performance compared to the conventional Cox regression model.