Identifying gene expression-based biomarkers in online learning environments.

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning in Medicine

Background:

• Gene expression-based classifiers are crucial for patient stratification but suffer from accuracy degradation over time due to concept drift.
• Traditional data mining techniques are insufficient for addressing the dynamic nature of gene expression data and evolving clinical relevance of biomarkers.

Purpose of the Study:

• To develop an online ensemble learning method for continuous validation and adjustment of gene expression biomarker panels.
• To propose a computational solution for feature drift, ensuring biomarker relevance over time.
• To improve the accuracy and robustness of breast tumor subtyping classifiers.

Main Methods:

• An online ensemble learning framework was developed to adapt to new data and mitigate concept drift.
• A computational approach was implemented to address feature drift in gene expression signatures.
• A large-scale transcriptomic dataset (SCAN-B and TCGA-BRCA, ~3500 patients) was used for benchmark studies on breast tumor classification.

Main Results:

• The proposed online learning strategy significantly improved the classification performance of established biomarker panels (PAM50, OncotypeDX, Endopredict).
• The method successfully incorporated clinically relevant features, enhancing biomarker utility.
• Newly developed biomarker models demonstrated sustained high classification accuracy despite changes in gene expression data sources.

Conclusions:

• Online ensemble learning offers a robust solution for maintaining and improving the accuracy of gene expression-based classifiers in the face of concept and feature drift.
• This approach enhances the reliability of biomarkers for clinical applications like breast tumor subtyping.
• The developed method provides a framework for adaptive biomarker discovery and validation in dynamic biological datasets.