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Table 4 Classification Results on the GBM Dataset

From: A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

Method

ACC

F1_weighted

F1_macro

Precision

Recall

RF

0.807

0.804

0.800

0.840

0.790

KNN

0.757

0.755

0.754

0.789

0.774

Lasso

0.783

0.784

0.787

0.795

0.782

XGBoost

0.783

0.782

0.771

0.793

0.764

MoGCN

0.840

0.843

0.834

0.841

0.842

MOGONET

0.831

0.833

0.821

0.820

0.824

Transformer+GCN

0.855

0.859

0.853

0.868

0.867

S3VM

0.843

0.839

0.830

0.869

0.818

SEGCN

0.867

0.865

0.857

0.892

0.836

MOSEGCN

0.892

0.890

0.897

0.905

0.884

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BMC Genomics

ISSN: 1471-2164

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