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Table 1 The best predictive model and gene signature for discrimination of metal from non-metal toxicants

From: Development of predicitve models to distinguish metals from non-metal toxicants, and individual metal from one another

No. of probe sets

Cross validation error

Prediction error (D2 to D1)

Probe set ID

D2

C

1

8

8

9

A_44_P915194(FAM174B)

5

4

5

8

+ A_42_P546708(KHDRBS3), A_44_P1034910(RTN2), A_42_P537091(FAM12B), A_43_P11261(AHNAK2)

7

3

4

5

+ A_44_P593735(TC632928), A_42_P829301(SLC1A5)

8

3

3

4

+ A_44_P427814(IGH-6)

10

2

3

2

+ A_42_P537051(FAM70B), A_44_P1005988(CDIG2)

14

0

1

1

+ A_43_P11561(ARNT), A_44_P1011716(ADORA2B),

A_43_P11444(S100G), A_44_P608892(TC596871)

15

0

0

0

+ A_43_P11861(DIO3)

16

0

1

1

+ A_44_P175654

18

2

1

1

+ A_44_P1040207, A_44_P426107

25

2

2

1

+ A_43_P21000, A_44_P299835,A_44_P1040926

A_44_P751206

A_44_P961496

A_44_P471440

A_43_P21816