Visual analytics framework for survival analysis and biomarker discovery from gene expression data

Kokošar, Jaka (avtor), Turkay, Cagatay (avtor), Ausec, Luka (avtor), Štajdohar, Miha (avtor), Zupan, Blaž (avtor)

We introduce a visual analytics methodology for survival analysis, and propose a framework that defines a reusable set of visualization and modeling components to support exploratory and hypothesis-driven biomarker discovery. Survival analysis—essential in biomedicine—evaluates patients‘ survival rates and the onset of medically relevant events, given their clinical and genetic profiles and genetic predispositions. Existing approaches often require programming expertise or rely on inflexible analysis pipelines, limiting their usability among biomedical researchers. The lack of advanced, user-friendly tools hinders problem solving, limits accessibility for biomedical researchers, and restricts interactive data exploration. Our methodology emphasizes functionality-driven design and modularity, akin to combining LEGO bricks to build tailored visual workflows. We (1) define a minimal set of reusable visualization and modeling components that support common survival analysis tasks, (2) implement interactive visualizations for discovering survival cohorts and their characteristic features, and (3) demonstrate integration within an existing visual analytics platform. We implemented the methodology as an open-source add-on to Orange Data Mining and validated it through use cases ranging from Kaplan–Meier estimation to biomarker discovery. While the framework is generally applicable, we illustrate its value through case studies in cancer research, where survival analysis is of critical importance. The resulting framework illustrates how methodological design can drive intuitive, transparent, and effective survival analysis.

• Jezik: Angleški jezik
• Ključne besede: survival analysis, visual analytics, biomarker discovery, gene expression data, data analysis workflows
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2026
• Št. strani: 19 str.
• Številčenje: Vol. 21, iss. 3, art. e0325399
• PID: 20.500.12556/RUL-181155
• UDK: 004:57
• ISSN pri članku: 1932-6203
• DOI: 10.1371/journal.pone.0325399
• COBISS.SI-ID: 273102339
• Datum objave v RUL: 26.03.2026

Gradivo je del revije

• Naslov: PloS one
• Založnik: Public Library of Science
• ISSN: 1932-6203
• COBISS.SI-ID: 2005896

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: analiza preživetja, vizualna analitika, odkrivanje biomarkerjev, podatki o izražanju genov