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Table 2 Predictive performance (AUC) for the neuroblastoma dataset

From: HetEnc: a deep learning predictive model for multi-type biological dataset

  Model RNA-seq Microarray
FAV OS_All OS_HR FAV OS_All OS_HR
Cross-validation HetEnc 0.964
(0.009)
0.830
(0.019)
0.520
(0.044)
0.962
(0.011)
0.849
(0.024)
0.651
(0.044)
HetEnc 0.969
(0.007)
0.854
(0.024)
0.592
(0.027)
0.948
(0.015)
0.825
(0.016)
0.569
(0.022)
Raw-DNN* 0.926
(0.043)
0.698
(0.058)
0.578
(0.03)
0.906
(0.054)
0.721
(0.035)
0.568
(0.031)
FS-DNN* 0.923
(0.052)
0.704
(0.046)
0.558
(0.028)
0.919
(0.056)
0.722
(0.047)
0.559
(0.025)
External Testing
(on same testing set)
KNN 0.896
(0.032)
0.641
(0.032)
0.495
(0.048)
0.907
(0.035)
0.662
(0.031)
0.515
(0.041)
NSC 0.901
(0.036)
0.700
(0.048)
0.499
(0.036)
0.921
(0.032)
0.713
(0.067)
0.510
(0.035)
SVM 0.894
(0.043)
0.631
(0.024)
0.512
(0.050)
0.914
(0.035)
0.620
(0.034)
0.525
(0.047)
RandomForest 0.905
(0.014)
0.740
(0.019)
0.563
(0.030)
0.912
(0.012)
0.727
(0.020)
0.560
(0.030)
XGBoost 0.883 0.742 0.517 0.874 0.749 0.611
Avg. of Best 60 SEQC Models 0.931
(0.02)
0.735
(0.072)
0.544
(0.052)
0.929
(0.02)
0.756
(0.082)
0.563
(0.038)
  1. *Raw-DNN used the raw 10,042 gene features as input of DNN model, FS-DNN further applied feature selection threshold (p < 0.05 for each endpoint) before entering the DNN model. The structure of DNN model used in Raw-DNN and FS-DNN are the same as the DNN used in HetEnc supervised learning step