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This study presents a visual analytics framework for survival analysis, simplifying biomarker discovery for biomedical researchers. The open-source tool enhances data exploration and accessibility, making complex analyses more intuitive.

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Area of Science:

  • Biomedical data science
  • Visual analytics
  • Survival analysis

Background:

  • Survival analysis is crucial in biomedicine for evaluating patient outcomes based on clinical and genetic data.
  • Current methods often require programming skills and lack flexibility, hindering accessibility for researchers.
  • There is a need for user-friendly tools to facilitate interactive data exploration and biomarker discovery.

Purpose of the Study:

  • To introduce a visual analytics methodology and framework for survival analysis.
  • To support exploratory and hypothesis-driven biomarker discovery.
  • To enhance the accessibility and usability of survival analysis tools for biomedical researchers.

Main Methods:

  • Developed a modular framework with reusable visualization and modeling components for survival analysis tasks.
  • Implemented interactive visualizations for identifying survival cohorts and their features.
  • Integrated the framework into an open-source add-on for the Orange Data Mining platform.

Main Results:

  • The methodology enables intuitive, transparent, and effective survival analysis.
  • Validated through use cases including Kaplan-Meier estimation and biomarker discovery.
  • Demonstrated value in cancer research, a field where survival analysis is critical.

Conclusions:

  • The proposed framework improves accessibility and interactivity in survival analysis.
  • Functionality-driven design and modularity facilitate tailored visual workflows.
  • This approach empowers biomedical researchers in biomarker discovery and patient outcome analysis.