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Table 2 Comparison with Other methods

From: A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction

Methods

OS

DSS

PFI

DFI

SVM

0.6905

(±0.0108)

0.6927

(±0.0154)

0.6416

(±0.0119)

0.5950

(±0.0174)

DecisionTree

0.6973

(±0.0082)

0.6877

(±0.0199)

0.6503

(±0.0093)

0.5736

(±0.0276)

NaĂ¯ve Bayes

0.6825

(±0.0110)

0.7139

(±0.0277)

0.6672

(±0.0074)

0.6631

(±0.0304)

kNN

0.7189

(±0.0086)

0.7134

(±0.0146)

0.6788

(±0.0095)

0.6488

(±0.0474)

RandomForest

0.7355

(±0.0082)

0.7449

(±0.0160)

0.6999

(±0.0134)

0.6621

(±0.0299)

AdaBoost

0.7297

(±0.0042)

0.7369

(±0.0219)

0.6831

(±0.0155)

0.6454

(±0.0254)

Multi-view Factorization

AutoEncoder [3]

0.766

(−)

–

0.724

(−)

–

MOSAE

0.7830

(±0.0081)

0.7870

(±0.0293)

0.7325

(±0.0123)

0.7061

(±0.0393)