Table 5 Classification rules identified by Logic Learning Machine applied to gene expression profiles in eight selected data sets for cancer diagnosis

 Monoc. Gamm.

SNHG3_1 ≤ 9.28

SNORA14B ≤ 4.30

–

 Monoc. Gamm.

Control_3389 ≤ 8.20

–

–

 MM

THOP1 > 6.23

TARP_5 ≤ 6.71

–

 MM

C22orf23 ≤ 5.20

FLJ20712 ≤ 3.14

–

 Smold. MM

DNAJC7 > 8.13

IGK_2 ≤ 10.4561

DEK > 6.50

 Smold. MM

HNRNPA1 > 6.44

–

–

 HC

AQP7 ≤ 8.46

–

–

 Non tumor

CLPX_1 > 11.4116

–

–

 Normal cells

DSCC1_1 ≤ 110.1

–

–

 SCLC

CBX3_1 > 2232.75

–

–

 Breast cancer

FMN2 < = 116.32

–

–

 Fibroblast

SHC4 > 52.20

–

–

 Classic MB

EFHD2_1 > 3.87

LOC100132891 ≤ 4.37

–

 Classic MB

TCL1A > 4.66

–

–

 Other MB

LOC100132891 > 4.18

5.47 < ZMYM5_3 ≤ 6.17

 Other MB

CHIAP2 > 3.38

ZNF212 ≤ 6.45

–

 Colon cancer

KLK6 > 7.71

–

–

 Melanoma

EDNRB > 5.72279

–

–

 Non-SCLC

5.61 < TMEM51 ≤ 6.55

FAM177A1 > 8.27

LINC00936 > 5.59

 Ovarian cancer

TMEM101 ≤ 6.15

–

–

 Ovarian cancer

MEIS1_1 > 6.70

–

–

 Renal cancer

LRRN4 > 4.69

APBB1IP_2 > 7.46

–

 Benign disease^a

2.32 < IGHV7–81 ≤ 3.29

2.87 < BM983749 ≤ 4.06

LIM2 > 4.11

 Benign disease^a

LCP2_1 > 9.07

ST8SIA2_1 ≤ 2.215

–

 Ectopic cancers

ST3GAL1 > 6.55

PWWP2A > 6.18

–

 Healthy controls

USMG5 > 11.85

–

–

 Healthy controls

NUFIP2_1 > 8.81

–

–

 Breast cancer^a

MKNK1 ≤ 3.91

227762_at ≤8.3

BF194770 > 2.385

 Breast cancer

ZNF81 ≤ 2.99

MMAB_1 ≤ 4.095

–

 Breast cancer

AU143882 > 4.57

–

–

 Untreated controls

COQ10A < = 125.66

–

–

 Renal cancer

COQ10A > 125.66

–

–