Table 8.

Pseudo-code of informative genes selection

Algorithm 1 Informative gene selection (Dateset, GRank)

Require: Dateset is a binary-class training dataset with n samples

Require: GRank is the order of all p genes {GRank1, GRank2,…, GRankj,…, GRankp}

Ensure: Returns the binary-discriminative informative genes subset of Dateset

1: ture_Y ← class lable of training samples

2: j ← 1; MCCbenchmark ← 0; B ← 100

3: repeat

4: S ← GRankj # introducing GRankj

5: if |S| ≤ 1 then

6: for i = 1 to n do # leave-one-out cross-validation

7: Y i ← +

8: get RS GRankj(+)

9: Y i ← −

10: get RS GRankj(−)

11: if RS GRankj(+) > RS GRankj(−) then pred_Y i ← +

12: else pred_Y i ← −

13: end for

14: MCCbenchmark ← get MCC (true_Y, pred_Y) from formula (14)

15: else

16: for i = 1 to n do # leave-one-out cross-validation

17: Y i ← +

18: get RS-net(+) from formula (15)

19: Y i ← −

20: get RS-net(−) from formula (15)

21: if RS-net(+) > RS-net (−) then pred_Y i ← +

22: else pred_Y i ← −

23: end for

24: MCC ← get MCC (true_Y, pred_Y) from formula (14)

25: end if

26: if MCC > MCCbenchmark then MCCbenchmark ← MCC

27: else delete GRankj

28: until j > B

29: retrun S