Artificial neural network classifier predicts neuroblastoma patients’ outcome

BMC Bioinformatics

Table 4 Hypoxia-related gene sets enriched in patients classified as Poor outcome

Gene set^a	ES^b	NES^c	FDR q-value^d
WINTER_HYPOXIA_UP	0.72	2.22	0.00
HARRIS_HYPOXIA	0.52	1.90	0.02
JIANG_HYPOXIA_CANCER	0.42	1.83	0.03
ELVIDGE_HYPOXIA_BY_DMOG_DN	0.46	1.76	0.03
NB-HYPO_62-PBSETS	0.53	1.65	0.06
WACKER_HYPOXIA_TARGETS_OF_VHL	0.60	1.61	0.06
KRIEG_HYPOXIA_VIA_KDM3A	0.42	1.64	0.06
KIM_HYPOXIA	0.48	1.59	0.06
MENSE_HYPOXIA_UP	0.44	1.58	0.05
LEONARD_HYPOXIA	0.45	1.47	0.08
WEINMANN_ADAPTATION_TO_HYPOXIA_DN	0.36	1.19	0.24

^aHypoxia-related gene sets enriched in the GSEA analysis
^bES (enrichment score) is the maximum deviation from zero encountered in a random walk for a gene set
^cNES (normalized enrichment score) is the fraction between the ES and the mean of the ES against a number of permutations of the dataset
^dFDR q-value is the estimated probability that the normalized enrichment score represents a false positive finding. Values <= 0.25 are considered acceptable

ISSN: 1471-2105