Language

English

Publication Date

3-1-2026

Journal

PLOS Computational Biology

DOI

10.1371/journal.pcbi.1014046

PMID

41860996

PMCID

PMC13004506

PubMedCentral® Posted Date

3-20-2026

PubMedCentral® Full Text Version

Post-print

Abstract

In biomedicine, survival analysis addresses time-to-event data to study outcomes like patient survival and treatment response, and supports biomarker discovery. Yet, teaching this analysis is often hindered by mathematical and programming barriers. We present a structured, hands-on tutorial that goes beyond a typical online guide-offering integrated video lectures, literature, quizzes, and practical exercises. Built around Orange Data Mining, an open and free no-code visual analytics platform, the tutorial covers key concepts such as censoring, Kaplan-Meier curves, group comparisons, and biomarker discovery through real-world datasets. Organized in four pedagogical units, it progresses from basic survival data analysis to gene and gene-set biomarker discovery. Designed for 2-3 hours of learning, it supports both individual study and classroom use, and was successfully tested with over 120 participants.

Keywords

Humans, Biomarkers, Computational Biology, Survival Analysis, Data Mining, Kaplan-Meier Estimate

Published Open-Access

yes

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