Online tutorial on survival analysis for biomarker discovery
Jaka Kokošar, Ela Praznik, Martin Špendl, Nancy P. Moreno, Alana Newell, Gad Shaulsky, Blaž Zupan

TL;DR
This paper introduces an accessible, hands-on tutorial for survival analysis and biomarker discovery using a no-code platform, suitable for both self-learners and educators.
Contribution
A structured, no-code tutorial for survival analysis and biomarker discovery that integrates video lectures, exercises, and real-world datasets.
Findings
The tutorial was successfully tested with over 120 participants and supports both individual and classroom learning.
It covers key survival analysis concepts and progresses to biomarker discovery using real-world datasets.
The tutorial is built on Orange Data Mining, an open and free platform, ensuring accessibility and ease of use.
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…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsAI in cancer detection · Gene expression and cancer classification · Cell Image Analysis Techniques
