# Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification

arXiv: https://arxiv.org/abs/2503.13078 · HTML: https://mlsanity.com/paper/2503.13078/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Lasso Meets Horseshoe : A Survey](https://mlsanity.com/paper/1706.10179/) (2017)
- [Multivariate Bayesian structured variable selection for pharmacogenomic studies](https://mlsanity.com/paper/2101.05899/) (2021)
- [Variational Bayes for high-dimensional proportional hazards models with applications within gene expression](https://mlsanity.com/paper/2112.10270/) (2021)
- [Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression](https://mlsanity.com/paper/2004.07542/) (2020)
- [BayesSUR: An R package for high-dimensional multivariate Bayesian variable and covariance selection in linear regression](https://mlsanity.com/paper/2104.14008/) (2021)

## Built on by (most cited first, 1 of 1)

- [Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis](https://mlsanity.com/paper/2509.01001/) (2025)

## Abstract (arXiv)

An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge can produce non-interpretable results. We propose a penalized semiparametric Bayesian Cox model with graph-structured selection priors for sparse identification of multi-omics features by making use of a biologically meaningful graph via a Markov random field (MRF) prior to capturing known relationships between multi-omics features. Since the fixed graph in the MRF prior is for the prior probability distribution, it is not a hard constraint to determine variable selection, so the proposed model can verify known information and has the potential to identify new and novel biomarkers for drawing new biological knowledge. Our simulation results show that the proposed Bayesian Cox model with graph-based prior knowledge results in more trustable and stable variable selection and non-inferior survival prediction, compared to methods modeling the covariates independently without any prior knowledge. The results also indicate that the performance of the proposed model is robust to a partially correct graph in the MRF prior, meaning that in a real setting where not all the true network information between covariates is known, the graph can still be useful. The proposed model is applied to the primary invasive breast cancer patients data in The Cancer Genome Atlas project.