ArticlePLoS computational biology2023
SurvivalPath:A R package for conducting personalized survival path mapping based on time-series survival data.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Prognostic Significance and Immune Landscape of Neuroendocrine Differentiation-Related Genes in Non-Small Cell Lung Cancer.Biological procedures online · 2026Article
- A Machine Learning Framework for Cancer Prognostics: Integrating Temporal and Immune Gene Dynamics via ARIMA-CNN.Biomedicines · 2025Article
- Survival path model outperforms conventional static machine learning models in long-term dynamic prognosis prediction for patients with intermediate stage hepatocellular carcinoma.Bioinformatics advances · 2025Article
- Comprehensive analysis of single-cell and bulk transcriptomes reveals key B-cell genes and immune microenvironment regulation in bladder cancer.Frontiers in immunology · 2025Article
- Landscape of the immune infiltration and identification of molecular diagnostic markers associated with immune cells in patients with kidney transplantation.Scientific reports · 2024Article
- Machine Learning for Dynamic Prognostication of Patients With Hepatocellular Carcinoma Using Time-Series Data: Survival Path Versus Dynamic-DeepHit HCC Model.Cancer informatics · 2024Article
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Authors and funding
9 authors.
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Abstract
The survival path mapping approach has been proposed for dynamic prognostication of cancer patients using time-series survival data. The SurvivalPath R package was developed to facilitate building personalized survival path models. The package contains functions to convert time-series data into time-slices data by fixed interval based on time information of input medical records. After the pre-processing of data, under a user-defined parameters on covariates, significance level, minimum bifurcation sample size and number of time slices for analysis, survival paths can be computed using the main function, which can be visualized as a tree diagram, with important parameters annotated. The package also includes function for analyzing the connections between exposure/treatment and node transitions, and function for screening patient subgroup with specific features, which can be used for further exploration analysis. In this study, we demonstrate the application of this package in a large dataset of patients with hepatocellular carcinoma, which is embedded in the package. The SurvivalPath R package is freely available from CRAN, with source code and documentation hosted at https://github.com/zhangt369/SurvivalPath.
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