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Table 3 Temporal-longitudinal scenario: comparison between SESglmm and glmmLasso based on 20 replications with different target variable (gene) and independently randomly selected 2000 genes as predictor variables

From: Feature selection for high-dimensional temporal data

Dataset

GDS5088

GDS4395

GDS4822

GDS3326

GDS3181

GDS4258

GDS3432

GDS3915

Average difference

-3.560(4.118)

0.188(0.516)

-0.003(0.134)

-0.180(0.506)

-0.020(0.04)

-0.139(0.288)

0.000(0.355)

0.093(0.455)

Proportion

19/20

7/20

9/20

13/20

15/20

10/20

10/20

8/20

p-value

0.0001 a

0.128

0.938

0.0015 a

0.0312 b

0.024 b

0.9946

0.3842

  1. Average difference in performances (standard deviation of the differences appear inside the parentheses) and percentage of times SESglmm outperformed glmmLasso. The last line contains the permutation based p-value for the equality of the mean performances. Symbols a and b denote average differences that are statistically significant at 0.01 and 0.05, respectively. Notice that SESglmm is either statistically significantly better or on par with glmmLasso in terms of predictive performance
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BMC Bioinformatics

ISSN: 1471-2105

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