# A network-constrain Weibull AFT model for biomarkers discovery

arXiv: https://arxiv.org/abs/2402.18242 · HTML: https://mlsanity.com/paper/2402.18242/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 69 references that are in the map.
- First posted on arXiv 28 Feb 2024.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima](https://mlsanity.com/paper/1305.2436/) (2013)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [Penalized Estimation of Frailty-Based Illness-Death Models for Semi-Competing Risks](https://mlsanity.com/paper/2202.00618/) (2022)
- [Cross validation approaches for penalized Cox regression](https://mlsanity.com/paper/1905.10432/) (2019)
- [Scalable algorithms for semiparametric accelerated failure time models in high dimensions](https://mlsanity.com/paper/2104.01707/) (2021)

## Abstract (arXiv)

We propose AFTNet, a novel network-constraint survival analysis method based on the Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for variable selection and estimation. When using the log-linear representation, the inference problem becomes a structured sparse regression problem for which we explicitly incorporate the correlation patterns among predictors using a double penalty that promotes both sparsity and grouping effect. Moreover, we establish the theoretical consistency for the AFTNet estimator and present an efficient iterative computational algorithm based on the proximal gradient descent method. Finally, we evaluate AFTNet performance both on synthetic and real data examples.