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Models/HMCN-F

HMCN-F

Reported on 16 benchmarks across 1 task

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology16 results

  • Multi-Label ClassificationonDerisi Funcat
    AU(PRC)
    0.193
    best: 0.195 (C-HMCNN)
  • Multi-Label ClassificationonSpo Funcat
    AU(PRC)
    0.211
    best: 0.215 (C-HMCNN)
  • Multi-Label ClassificationonCellcycle Funcat
    AU(PRC)
    0.252
    best: 0.255 (C-HMCNN)
  • Multi-Label ClassificationonExpr Funcat
    AU(PRC)
    0.301
    best: 0.302 (C-HMCNN)
  • Multi-Label ClassificationonSeq Funcat
    AU(PRC)
    0.291
    best: 0.292 (C-HMCNN)
  • Multi-Label ClassificationonGasch1 Funcat
    AU(PRC)
    0.284
    best: 0.286 (C-HMCNN)
  • Multi-Label ClassificationonGasch2 Funcat
    AU(PRC)
    0.254
    best: 0.258 (C-HMCNN)
  • Multi-Label ClassificationonExpr GO
    AU(PRC)
    0.452
  • Multi-Label ClassificationonEisen Funcat
    AU(PRC)
    0.298
    best: 0.306 (C-HMCNN)
  • Multi-Label ClassificationonSpo GO
    AU(PRC)
    0.376
    best: 0.382 (C-HMCNN)
  • Multi-Label ClassificationonEisen GO
    AU(PRC)
    0.44
    best: 0.455 (C-HMCNN)
  • Multi-Label ClassificationonGasch1 GO
    AU(PRC)
    0.428
    best: 0.436 (C-HMCNN)
  • Multi-Label ClassificationonCellcycle GO
    AU(PRC)
    0.4
    best: 0.413 (C-HMCNN)
  • Multi-Label ClassificationonGasch2 GO
    AU(PRC)
    0.465
  • Multi-Label ClassificationonDerisi GO
    AU(PRC)
    0.369
    best: 0.37 (C-HMCNN)
  • Multi-Label ClassificationonSeq GO
    AU(PRC)
    0.447