Ensemble methods of rank-based trees for single sample classification with gene expression profiles

Table 1 Binary and multi-class datasets of gene expression profiles for cancer discrimination

Datasets^a	Platform	\(N\)^b	\(P\) ^b	\(K\) ^b	Class sample size	References
Liver	cDNA	180	85	2	HCC/liver = 104/76	[41]
CNS	Affy	34	857	2	CMD/DMD = 25/9	[42]
Glioblastoma	Affy	22	1152	2	CO/NO = 7/15	[43]
Prostate	Affy	77	339	2	PR/N = 58/19	[44]
NHL	cDNA	42	1095	2	DLBCL\(_1\)/DLBCL\(_2\) = 21/21	[45]
Breast	Affy	49	1198	2	ER+/ER− = 25/24	[46]
SRBCTs	cDNA	83	1069	4	BL/EWS/NB/RMS = 29/11/18/25	[47]
Leukemia	Affy	72	2194	3	MLL/ALL/AML = 24/20/28	[48]
Lung	Affy	203	1543	5	ADE/SQU/SCC/NO = 139/17/6/21/20	[49]
Bladder	Affy	40	1203	3	C1/C2/NO = 9/20/11	[50]
ALL	Affy	248	2526	6	TALL/E2A/BCR/TEL/MLL/NO = 15/27/64/20/79/43	[51]
TNBC	Affy & RNAseq^c	375	2188	4	BL1/BL2/M/LAR = 125/80/67/103	[37]

^aCNS central nervous system, AODs anaplastic oligodendrogliomas, NHL Non-Hodgkin’s lymphoma, SRBCTs small round blue cell tumors, ALL acute lymphoblastic leukemia, TNBC triple negative breast cancer
^bN stands for number of samples, P for number of genes and K for number of classes
^cWe downloaded other three datasets [38,39,40] and trained our models on data from one platform (e.g. microarray) while tested its prediction performance on data from another platform (e.g. RNA-seq)

ISSN: 1479-5876