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Online tutorial on survival analysis for biomarker discovery.
Jaka Kokošar1, Ela Praznik1, Martin Špendl1
1Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Plos Computational Biology
|March 20, 2026
Summary
This tutorial simplifies survival analysis for biomedicine using a no-code platform, making time-to-event data analysis and biomarker discovery accessible to more researchers.
Area of Science:
- Biomedicine
- Data Science
- Bioinformatics
Background:
- Survival analysis is crucial for time-to-event data in biomedicine, aiding patient survival studies, treatment response evaluation, and biomarker discovery.
- Traditional teaching methods face challenges due to mathematical complexity and programming prerequisites.
- Existing online resources often lack the comprehensive, integrated approach needed for effective learning.
Purpose of the Study:
- To present a structured, hands-on tutorial for learning survival analysis and biomarker discovery.
- To overcome the mathematical and programming barriers often associated with survival analysis.
- To provide an accessible learning resource using a no-code visual analytics platform.
Main Methods:
- The tutorial utilizes Orange Data Mining, a free, open-source, no-code visual analytics platform.
- It incorporates integrated video lectures, literature, quizzes, and practical exercises.
- The content is organized into four pedagogical units, covering basic survival analysis to gene and gene-set biomarker discovery.
Main Results:
- The tutorial effectively teaches key survival analysis concepts including censoring, Kaplan-Meier curves, and group comparisons.
- It enables practical application of these concepts to real-world datasets for biomarker discovery.
- Successfully tested with over 120 participants, indicating high usability and effectiveness.
Conclusions:
- This tutorial offers an accessible and effective method for learning survival analysis and biomarker discovery in biomedicine.
- The no-code approach significantly lowers the barrier to entry for researchers without extensive programming or statistical backgrounds.
- The integrated learning format supports both self-paced individual study and structured classroom instruction.
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