Design of a multi-signature ensemble classifier predicting neuroblastoma patients' outcome

BMC Bioinformatics

Table 2 Twenty signatures selected for NB-MuSE-classifier construction

Signature*	PMID	Reference	Genes^	Category^^	Features°
Chen	19921788	[22]	50	biology	MYCN
De Preter II	16989664	[1]	73	biology	Neuroblast transformation
Di Pietro	19402918	[23]	33	biology	Apoptosis
Fardin	20624283	[12]	62	biology	Tumor hypoxia
Fransson	17044048	[24]	10	biology	Chromosome 1p36
Fredlund	18780787	[25]	103	biology	MYCN expression
Hahn	18607002	[26]	14	biology	Histone deacetylase
McArdle	15090470	[29]	10	biology	Chromosome 11q
Nevo II	15081541	[31]	13	biology	CXCR4 receptor
Oe	15992370	[28]	117	biology	response to NGF
Shimada	17941064	[27]	5	biology	Biomarkers
Asgharzadeh	16954472	[20]	44	stratification	MYCN non amplified
Benard	19383347	[21]	37	stratification	age groups
De Preter I	20179214	[10]	42	stratification	Risk/outcome
Fischer	16951229	[8]	17	stratification	St4 vs St4s
Nevo II	19739072	[30]	95	stratification	St1 vs St4
Oberthuer	17075126	[7]	108	stratification	Risk/outcome
Ohira	15837623	[6]	39	stratification	Risk/outcome
Vermeulen	19515614	[9]	59	stratification	Risk/outcome
Wei	15466177	[4]	12	stratification	Risk/outcome

* First author's name of the paper describing the neuroblastoma-related signature used. The signatures predicate patients' outcome with an accuracy > 80%.
^{^} Number of genes comprising the signature.
^{^^} Categories indicative of the natureof the signature. Biology: the signature discriminates biologically defined situations. Stratification: the signature discriminates classes of tumors derived from patients' stratification.
° Features: Indications on the nature of the biological questions asked or stratification criteria used. St = INSS stage.

ISSN: 1471-2105