BayesSurvive-package: BayesSurvive: Bayesian Survival Models for High-Dimensional...

In BayesSurvive: Bayesian Survival Models for High-Dimensional Data

BayesSurvive: Bayesian Survival Models for High-Dimensional Data

Description

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s12859-021-04483-z")}) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2503.13078")}).

Author(s)

Maintainer: Zhi Zhao [e-mail]

Authors:

• Waldir Leoncio

• Katrin Madjar

• Tobias Østmo Hermansen

Other contributors:

• Manuela Zucknick [contributor]

• Jörg Rahnenführer [contributor]

See Also

Useful links:

• https://github.com/ocbe-uio/BayesSurvive

• Report bugs at https://github.com/ocbe-uio/BayesSurvive/issues

BayesSurvive documentation built on April 3, 2025, 5:58 p.m.