NetSurvProx: 'NetSurvProx': Network-Based Survival Analysis via Proximal
Methods
Introduces a novel network-constrained survival
analysis framework for variable selection and parameter estimation in
penalized survival models with convex penalties. The package extends two
classical survival models, the Cox Proportional Hazards (PH) model and the
Accelerated Failure Time (AFT) model, by incorporating prior biological
knowledge from curated interaction networks (e.g., KEGG) into a
double-penalty framework. The first penalty enforces variable selection
through a LASSO penalty, while the second preserves gene-gene correlations
by incorporating Laplacian-based constraints, ensuring that biologically
relevant network structures are maintained. Using censored survival data,
the method enables the identification of predictive biomarkers and pathways
with potential relevance for target therapies. Model estimation is
performed via proximal optimization algorithms combined with
cross-validation for reliable tuning. To enhance interpretability,
dedicated utility functions are implemented to consolidate results,
yielding biologically coherent insights that can support personalized
medicine and contribute to improved patient outcomes.
| Version: |
1.0.0 |
| Depends: |
R (≥ 4.3) |
| Imports: |
AnnotationDbi, curl, cvTools, dplyr, flexsurv, foreach, ggplot2, ggpubr, glmnet, grDevices, Hmisc, httr, igraph, magic, openxlsx, RColorBrewer, rmarkdown, survAUC, survival, survminer |
| Suggests: |
knitr, org.Hs.eg.db, plotly, scales, sessioninfo, stringr, visNetwork |
| Published: |
2026-06-09 |
| DOI: |
10.32614/CRAN.package.NetSurvProx (may not be active yet) |
| Author: |
Maura Mecchi [aut, cre],
Antonella Iuliano [aut] |
| Maintainer: |
Maura Mecchi <maura.mecchi at unibas.it> |
| License: |
GPL (≥ 3) |
| NeedsCompilation: |
no |
| CRAN checks: |
NetSurvProx results |
Documentation:
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