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Table 3 The benchmark data.

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

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