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  3. Reduxis: A Comprehensive Framework For Robust Event-based Modeling And Profiling Of High-dimensional Biomedical Data.
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  3. Reduxis: A Comprehensive Framework For Robust Event-based Modeling And Profiling Of High-dimensional Biomedical Data.

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ReduXis: A Comprehensive Framework for Robust Event-Based Modeling and Profiling of High-Dimensional Biomedical Data.

Neel D Sarkar1, Raghav Tandon1,2, James J Lah3

  • 1Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

International Journal of Molecular Sciences
|September 27, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

ReduXis is a new pipeline that improves event-based models for disease progression by automating data readiness, selecting key biomarkers using ensemble voting, and providing interpretable results for Alzheimer's disease and cancer.

Keywords:
Alzheimer’s diseaseartificial intelligencebiomarker discoverycolorectal adenocarcinomadisease progressionevent-based modelingmachine learningmultimodal data integrationomics-driven profilingtransitional cell carcinoma

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Area of Science:

  • Computational Biology
  • Biomedical Informatics
  • Machine Learning in Medicine

Background:

  • Event-based models (EBMs) are valuable for analyzing biomarker changes in progressive diseases.
  • Challenges include data quality issues, high dimensionality, and limited interpretability of EBMs.
  • Existing methods require significant manual effort for data preparation and feature selection.

Purpose of the Study:

  • To introduce ReduXis, a streamlined pipeline designed to enhance the application of EBMs.
  • To address data quality, high dimensionality, and interpretability challenges in EBMs.
  • To facilitate the analysis of biomarker data for disease progression.

Main Methods:

  • Automated data readiness assessment upon dataset upload, including format verification, metadata completeness, and measurement compatibility checks.
  • Ensemble voting-based feature selection using gradient boosting, logistic regression, and random forest classifiers to identify robust biomarker subsets and prevent overfitting.
  • Generation of interpretable outputs such as subject-level staging, subtype assignments, comparative biomarker profiles, and classification performance visualizations.

    • Main Results:

    • ReduXis successfully automates data quality checks and provides actionable feedback.
    • The ensemble feature selection effectively identifies relevant biomarkers in high-dimensional data.
    • The pipeline generates clear, interpretable visualizations and assignments for downstream analysis.
    • Validation across Alzheimer's disease, transitional cell carcinoma, and colorectal adenocarcinoma cohorts demonstrates ReduXis's versatility.

    Conclusions:

    • ReduXis offers a robust and interpretable solution for applying event-based models to complex biomarker data.
    • The pipeline enhances transparency and facilitates downstream analysis in disease progression studies.
    • ReduXis has the potential to improve the utility of EBMs in clinical and research settings.