Speaker
Description
Apolipoprotein B (ApoB) is a key biomarker reflecting the number of atherogenic lipoprotein particles and is increasingly recognized as a superior indicator of cardiovascular risk compared to traditional lipid measures. However, direct ApoB measurement is not always routinely available in clinical practice, and existing tools do not provide an integrated framework for its estimation and interpretation. We introduce ApoBcomp, an open-access R/Shiny-based web application designed to estimate ApoB levels and translate lipid profile data into clinically meaningful risk insights. The platform enables users to upload standard lipid panel data (total cholesterol, HDL-C, and triglycerides) and compute ApoB estimates using both established equations and supervised machine-learning (ML) models. A total of ten ML algorithms, including linear-regression, lasso, support vector regression, random-forest, and extreme gradient-boosting, were implemented. Model development incorporates grid-search hyperparameter optimization, repeated cross-validation, and external validation to ensure robustness and generalizability across diverse datasets. Beyond estimation, ApoBcomp integrates cardiovascular risk modeling modules aligned with major clinical guidelines, enabling classification of individuals into clinically actionable risk categories. This feature transforms raw lipid measurements into interpretable outputs that can support both research and clinical decision-making processes. The application is implemented in R using a modular Shiny architecture, supporting real-time computation, interactive exploration, and reproducible reporting. Users can upload data, perform analyses, and export results within a unified interface. The fully functional application is publicly available at: https://biotools.erciyes.edu.tr/ApoBcomp/. By bridging statistical-modeling, machine-learning, and clinical interpretation, ApoBcomp provides a scalable and reproducible platform for ApoB estimation and cardiovascular risk assessment within the R ecosystem.
Web Application: https://biotools.erciyes.edu.tr/ApoBcomp/
Funding: This work was supported by Research Fund of the Erciyes University (Project No: TYP-2025-14947).
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Apolipoprotein B, Shiny, Machine-learning, Cardiovascular risk, Biomarker estimation |
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| Virtual Option | This submission is for onsite presentation primarily, but I would also like it to be considered for pre-recorded virtual presentation if I don't get an onsite slot |
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| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |