Deep Learning Framework for Integrating Multibatch Calibration, Classification, and Pathway Activities.

Area of Science:

• Bioinformatics
• Data Science
• Computational Biology

Background:

• High-throughput technologies have led to an explosion of biological data, creating new analytical challenges.
• Deep learning is a powerful technique, but domain discrepancy, especially across multiple batches, hinders classification accuracy.
• Existing pairwise adaptation methods are often suboptimal for multi-batch scenarios.

Purpose of the Study:

• To develop a joint deep learning framework for integrated batch effect removal, classification, and pathway activity analysis.
• To address the limitations of current methods in handling multi-batch domain discrepancy.
• To improve diagnostic accuracy and identify relevant biomarkers from biological data.

Main Methods:

• A novel joint deep learning framework was proposed.
• The framework integrates batch effect removal, classification, and downstream pathway activity analysis.
• The approach was validated on two MALDI MS-based metabolomics datasets.

Main Results:

• The proposed framework achieved the highest diagnostic accuracy (ACC).
• A significant improvement of approximately 10% in accuracy was observed compared to other methods.
• The approach demonstrated more effective batch effect removal than state-of-the-art methods.

Conclusions:

• The developed framework effectively removes batch effects from biological data.
• It yields more accurate classification and identifies superior biomarkers for disease diagnosis.
• This approach offers a more robust solution for analyzing multi-batch biological data.