Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems
Table 4
(a) Results of the experiment that evaluated the GRN prediction performance in predicting cascade motifs. (b) Results of the experiment that evaluated the GRN prediction performance in predicting cascade motifs.
(a)
Case
Total cascade motifs
Total number of “cascade motifs” that match with GS TRUE_CASCADE
Multiple Linear Regression total number of incorrect prediction due to “cascade errors” CASCADE_ERR
Percentage of cascade motifs in datasets
Set 1
gadE
105
29
3
0.16%
csgD
41
12
0
arcA
157
77
0
gadX
216
53
3
dcuR
21
15
0
marA
150
40
1
fis
658
173
3
Total
1348
399
10
Set 2
gadE
105
29
0
0.12%
csgD
41
12
0
arcA
157
77
0
gadX
216
53
0
dcuR
21
15
0
marA
150
40
0
fis
658
173
0
Total
1348
399
0
(b)
Case
Total cascade motifs
Total number of “cascade motifs” that match with GS TRUE_CASCADE
Multiple Linear Regression total number of incorrect prediction due to “cascade errors” CASCADE_ERR
Percentage of cascade motifs in datasets
Set 3
gadE
105
29
3
0.54%
csgD
41
12
0
arcA
157
77
14
gadX
216
53
8
dcuR
21
15
5
marA
150
40
7
fis
658
173
57
Total
1348
399
94
Note: () Percentage of cascade motifs in datasets ((Total cascade motifs – Total TRUE_CASCADE)/Total number of possible edges) 100. () Refer to Table 4 for the total number of possible edges. () Cascade motif is defined as A C for the case of A B C.
