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Table 2 Classification Results on the ROSMAP 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

AUC

F1

Precision

Recall

RF

0.754

0.755

0.759

0.774

0.745

KNN

0.651

0.649

0.673

0.655

0.691

Lasso

0.755

0.751

0.783

0.723

0.854

XGBoost

0.764

0.763

0.775

0.768

0.782

MoGCN

0.774

0.773

0.784

0.791

0.790

MOGONET

0.800

0.876

0.801

0.832

0.775

Transformer+GCN

0.802

0.803

0.804

0.827

0.782

S3VM

0.774

0.775

0.772

0.809

0.739

SEGCN

0.792

0.794

0.792

0.824

0.764

MOSEGCN

0.830

0.832

0.827

0.878

0.782

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BMC Genomics

ISSN: 1471-2164

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