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Table 3 The average MSE of predicting test expression values \(\tilde{\mathbf {X}}_{test}\) using different models

From: A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks

Method TF_Net PPI Genetics
Features Graph Features Graph Features Graph
GCN 7.791 ± 3.550 15.127 ± 2.280 6.208 ± 0.607 11.106 ± 0.52198 5.988 ± 0.696 4.560 ± 0.351
GraphSAGE 0.332 ± 0.160 8.078 ± 2.592 0.265 ± 0.135 2.844 ± 0.349 0.233 ± 0.127 4.466 ± 1.605
GraphConv 0.318 ± 0.154 13.812 ± 4.534 0.308 ± 0.139 3.094 ± 0.431 0.234 ± 0.116 5.226 ± 1.054
FeatGraphConv 0.285 ± 0.135 7.525 ± 2.941 0.244 ± 0.130 5.207 ± 1.476 0.201 ± 0.112 3.414 ± 0.691
LR-embedding 1.583 ± 0.200 2.279 ± 0.403 1.091 ± 0.166 1.453 ± 0.264 1.863 ± 0.332 1.653 ± 0.271
RF-embedding 1.945 ± 0.318 2.150 ± 0.363 1.472 ± 0.267 1.452 ± 0.267 1.883 ± 0.343 1.897 ± 0.351
MLP 0.424 ± 0.170 0.354 ± 0.153 0.332 ± 0.134
LR 0.215 ± 0.126 0.147 ± 0.105 0.108 ± 0.084
RF 0.507 ± 0.143 0.194  ± 0.103 1.882 ± 0.343
  1. (Bold indicates lowest error mean per category of experiments for each group of methods (end-to-end, indirect, or baseline)