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Table 5  Comparison of diagnosis accuracy with one source batch for training and another target batch for testing

From: Joint deep learning for batch effect removal and classification toward MALDI MS based metabolomics

Source

Target

Baseline

ComBat

Ratio_G

fSVA

ResNet

NormAE

Remove_R

Ours

1

2

0.753

0.778

0.798

0.773

0.791

0.805

0.852

0.889

1

3

0.813

0.797

0.858

0.836

0.803

0.812

0.856

0.879

2

1

0.799

0.817

0.821

0.857

0.824

0.827

0.839

0.875

2

3

0.828

0.851

0.818

0.829

0.852

0.866

0.833

0.870

3

1

0.876

0.861

0.864

0.854

0.868

0.889

0.863

0.907

3

2

0.763

0.759

0.754

0.824

0.805

0.799

0.821

0.884

Overall

0.805

0.811

0.819

0.829

0.824

0.833

0.844

0.884

  1. “Baseline” denotes classification based on raw input data without any calibration for batch effect removal
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BMC Bioinformatics

ISSN: 1471-2105

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