Schwarzerova Jana, Olesova Dominika, Jureckova Katerina, Kvasnicka Ales, Kostoval Ales, Friedecky David, Sekora Jiri, Pomenkova Jitka, Provaznik Valentyna, Popelinsky Lubos, Weckwerth Wolfram
Department of Functional and Evolutionary Ecology, Molecular Systems Biology (MOSYS), University of Vienna, Vienna 1010, Austria.
Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno 616 00, Czech Republic.
Bioinform Adv. 2025 Apr 4;5(1):vbaf073. doi: 10.1093/bioadv/vbaf073. eCollection 2025.
The increasing use of big data and optimized prediction methods in metabolomics requires techniques aligned with biological assumptions to improve early symptom diagnosis. One major challenge in predictive data analysis is handling confounding factors-variables influencing predictions but not directly included in the analysis.
Detecting and correcting confounding factors enhances prediction accuracy, reducing false negatives that contribute to diagnostic errors. This study reviews concept drift detection methods in metabolomic predictions and selects the most appropriate ones. We introduce a new implementation of concept drift analysis in predictive classifiers using metabolomics data. Known confounding factors were confirmed, validating our approach and aligning it with conventional methods. Additionally, we identified potential confounding factors that may influence biomarker analysis, which could introduce bias and impact model performance.
Based on biological assumptions supported by detected concept drift, these confounding factors were incorporated into correction of prediction algorithms to enhance their accuracy. The proposed methodology has been implemented in Semi-Automated Pipeline using Concept Drift Analysis for improving Metabolomic Predictions (SAPCDAMP), an open-source workflow available at https://github.com/JanaSchwarzerova/SAPCDAMP.
代谢组学中大数据和优化预测方法的使用日益增加，这需要与生物学假设相一致的技术来改善早期症状诊断。预测数据分析中的一个主要挑战是处理混杂因素——即影响预测但未直接纳入分析的变量。
检测和校正混杂因素可提高预测准确性，减少导致诊断错误的假阴性结果。本研究回顾了代谢组学预测中的概念漂移检测方法，并选择了最合适的方法。我们介绍了一种在使用代谢组学数据的预测分类器中进行概念漂移分析的新实现方式。已确认了已知的混杂因素，验证了我们的方法并使其与传统方法保持一致。此外，我们还识别出了可能影响生物标志物分析的潜在混杂因素，这些因素可能会引入偏差并影响模型性能。
基于检测到的概念漂移所支持的生物学假设，这些混杂因素被纳入预测算法的校正中以提高其准确性。所提出的方法已在使用概念漂移分析改进代谢组学预测的半自动管道（SAPCDAMP）中实现，这是一个可在https://github.com/JanaSchwarzerova/SAPCDAMP获取的开源工作流程。
