Candidate gene prioritization by network analysis of differential expression using machine learning approaches

BMC Bioinformatics

Table 3 The benchmark data.

	Gene Name	GEO accession number		Gene Name	GEO accession number
1	Abca1	GSE5496	21	Mbnl1	GSE14691
2	Btk	GSE2826	22	Mst1r, Ron	GSE16629
3	Cav1	GSE10849	23	MyD88	GSE6688
4	Cav3	GSE10848	24	Nos3, eNos	GSE1988
5	Cftr	GSE5715	25	Phgdh	GSE8555
6	Clcn1	GSE14691	26	Pmp22	GSE1947
7	Cnr1	GSE7694	27	PPARα	GSE6864
8	Emd	GSE5304	28	Prkag3, AMPK G3	GSE4065
9	Epas1, Hif-2	GSE16067	29	Pthlh, Pthrp	GSE17654
10	Esrra	GSE7196	30	Rab3a	GSE6527
11	Gap43	GSE12687	31	RasGrf1	GSE8425
12	Gnmt	GSE9809	32	Rbm15	GSE12628
13	Hdac1	GSE5583	33	Runx	GSE4911
14	Hdac2	GSE6770	34	Scd1	GSE2926
15	Hsf4	GSE12415	35	Slc26a4	GSE10587
16	Hspa1A, Hsp70.1	GSE11120	36	Srf	GSE13333
17	Il6	GSE411	37	Tgm2	GSE10285
18	Lhx1, Lim1	GSE4230	38	Zc3h12a	GSE14891
19	Lhx8	GSE11897	39	Zfp36, Tpp	GSE5324
20	Lmna	GSE5304	40	Zfx	GSE7069

The benchmark consists of 40 publicly available data sets originated from Affymetrix chips on which mice with (simple) knockout genes were tested against controls.

ISSN: 1471-2105