Evidence map›Paper›PMID 41860996›Full record

ArticlePLoS computational biology2026

Online tutorial on survival analysis for biomarker discovery.

Jaka Kokošar, Ela Praznik, Martin Špendl, Nancy P Moreno, Alana Newell, Gad Shaulsky, Blaž Zupan

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jaka KokošarFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Ela PraznikFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Martin ŠpendlFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
Nancy P MorenoDepartment of Education, Innovation and Technology, Baylor College of Medicine, Houston, Texas, United States of America.
Alana NewellDepartment of Education, Innovation and Technology, Baylor College of Medicine, Houston, Texas, United States of America.
Gad ShaulskyDepartment of Education, Innovation and Technology, Baylor College of Medicine, Houston, Texas, United States of America.ORCID https://orcid.org/0000-0002-0532-0551
Blaž ZupanFaculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.ORCID https://orcid.org/0000-0002-5864-7056

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

BiomarkersComputational BiologyData MiningHumansKaplan-Meier EstimateSurvival AnalysisBiomarkers

Identifiers

PMID41860996
PMCPMC13004506

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.