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Table 1 Performance comparison of pan-cancer driver genes prediction

From: Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism

Methods

AUC

AUPRC

MOGONET

0.8903 ± 0.0003

0.7922 ± 0.0010

Multi-omics fusion

0.9088 ± 0.0002

0.8246 ± 0.0007

RGCN

0.8973 ± 0.0002

0.8103 ± 0.0007

GCN

0.8855 ± 0.0002

0.7709 ± 0.0009

GAT

0.8576 ± 0.0003

0.6801 ± 0.0014

EMOGI

0.9044 ± 0.0003

0.8169 ± 0.0008

MTGCN

0.9116 ± 0.0002

0.8332 ± 0.0006

MRNGCN

0.9192 ± 0.0002

0.8446 ± 0.0006

  1. Bold values indicates the best performance
  2. AUC value and AUPRC comparison of our method MRNGCN and other comparison methods on pan-cancer dataset
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BMC Bioinformatics

ISSN: 1471-2105

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