Random generalized linear model: a highly accurate and interpretable ensemble predictor

BMC Bioinformatics

Table 2 Description of the 20 disease expression data sets

Data set	Samples	Features	Reference	Data set ID	Binary outcome
adenocarcinoma	76	9868	[32]	NA	most prevalent class vs others
brain	42	5597	[33]	NA	most prevalent class vs others
breast2	77	4869	[34]	NA	most prevalent class vs others
breast3	95	4869	[34]	NA	most prevalent class vs others
colon	62	2000	[35]	NA	most prevalent class vs others
leukemia	38	3051	[36]	NA	most prevalent class vs others
lymphoma	62	4026	[37]	NA	most prevalent class vs others
NCI60	61	5244	[38]	NA	most prevalent class vs others
prostate	102	6033	[39]	NA	most prevalent class vs others
srbct	63	2308	[40]	NA	most prevalent class vs others
BrainTumor2	50	10367	[41]	NA	Anaplastic oligodendrogliomas vs Glioblastomas
DLBCL	77	5469	[42]	NA	follicular lymphoma vs diffuse large B-cell lymphoma
lung1	58	10000	[43]	GSE10245	Adenocarcinoma vs Squamous cell carcinoma
lung2	46	10000	[44]	GSE18842	Adenocarcinoma vs Squamous cell carcinoma
lung3	71	10000	[45]	GSE2109	Adenocarcinoma vs Squamous cell carcinoma
psoriasis1	180	10000	[46, 47]	GSE13355	lesional vs healthy skin
psoriasis2	82	10000	[48]	GSE14905	lesional vs healthy skin
MSstage	26	10000	[49]	E-MTAB-69	relapsing vs remitting RRMS
MSdiagnosis1	27	10000	[50]	GSE21942	RRMS vs healthy control
MSdiagnosis2	44	10000	[49]	E-MTAB-69	RRMS vs healthy control

Sample size, number of features, original reference, data set IDs and outcomes for the 20 disease related gene expression data sets.

ISSN: 1471-2105