ArticleBioinformatics (Oxford, England)2024
Tutorial on survival modeling with applications to omics data.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Competing risk model with a nonparametric form of relative risks.Lifetime data analysis · 2026Article
- Construction and validation of a prognostic model for in-hospital multiple organ dysfunction syndrome in ICU patients with respiratory failure based on ultrasound and laboratory parameters.Journal of thoracic disease · 2026Article
- Multi-omics data integration using time-to event endpoint and supervised Cox penalized regression: a comprehensive review.Clinical and experimental medicine · 2026Review
- A large-scale neutral comparison study of survival models on low-dimensional data.Bioinformatics (Oxford, England) · 2026Article
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- Immune Ageing Clocks: A Methods-Oriented Review of Tasks, Modalities, Models, and Recalibration.Cells · 2026Review
- A clustering-stratified cross-validation framework for validating omics survival models: application to head and neck cancer.BMC medical research methodology · 2025Article
- Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.International journal of molecular sciences · 2025Article
- Review
- A Weibull mixture cure frailty model for high-dimensional covariates.Statistical methods in medical research · 2025Article
- Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis.International journal of molecular sciences · 2024Review
- Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.BMC medical informatics and decision making · 2024Article
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Authors and funding
4 authors.
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Abstract
motivationIdentification of genomic, molecular and clinical markers prognostic of patient survival is important for developing personalized disease prevention, diagnostic and treatment approaches. Modern omics technologies have made it possible to investigate the prognostic impact of markers at multiple molecular levels, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, and how these potential risk factors complement clinical characterization of patient outcomes for survival prognosis. However, the massive sizes of the omics datasets, along with their correlation structures, pose challenges for studying relationships between the molecular information and patients' survival outcomes.
resultsWe present a general workflow for survival analysis that is applicable to high-dimensional omics data as inputs when identifying survival-associated features and validating survival models. In particular, we focus on the commonly used Cox-type penalized regressions and hierarchical Bayesian models for feature selection in survival analysis, which are especially useful for high-dimensional data, but the framework is applicable more generally. AVAILABILITY AND IMPLEMENTATION: A step-by-step R tutorial using The Cancer Genome Atlas survival and omics data for the execution and evaluation of survival models has been made available at https://ocbe-uio.github.io/survomics.
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