S. Table 1 – Breast cancer data sets
Data set
GSE2034
GSE4922
GSE6532
GSE7390
GSE11121
TCGA
High risk
93
30
23
36
28
40
Low risk
183
103
77
154
154
76
Total
276
133
100
190
182
116
Discarded
10
156
227
8
18
388
Samples were divided into two different risk groups according to whether the distant metastasis occurs within
five years [1].
S. Table 2- The performances of the 55 weak classifiers
Module name
G-protein coupled receptor protein signaling
pathway
cell adhesion
cell aging
cell communication
DNA repair
actin cytoskeleton organization
RNA splicing
response to heat
cell death
embryonic limb morphogenesis
cell-cell signaling
organ morphogenesis
apoptosis
cellular calcium ion homeostasis
response to DNA damage stimulus
positive regulation of transcription from RNA
polymerase II promoter
DNA recombination
positive regulation of inflammatory response
potassium ion transport
regulation of cell cycle
innate immune response
negative regulation of apoptosis
autophagy
post-translational protein modification
response to peptide hormone stimulus
neuron projection development
transmembrane transport
AUC
0.679
Module size
53
0.669
0.660
0.655
0.654
0.653
0.645
0.643
0.642
0.641
0.638
0.633
0.632
0.631
0.631
0.630
124
17
18
47
25
54
8
55
30
82
86
186
48
34
138
0.630
0.629
0.629
0.627
0.623
0.622
0.622
0.620
0.620
0.620
0.618
14
7
49
19
23
86
5
20
46
35
88
negative regulation of signal transduction
cell migration
response to stress
regulation of cell proliferation
inflammatory response
skeletal system development
cell division
cytokinesis
transport
interspecies interaction between organisms
response to vitamin A
meiosis
response to ethanol
anti-apoptosis
positive regulation of peptidyl-serine
phosphorylation
ion transport
positive regulation of NF-kappaB transcription
factor activit
microtubule cytoskeleton organization
protein homooligomerization
cytokine-mediated signaling pathway
response to mechanical stimulus
cell-cell adhesion
axon guidance
chemotaxis
protein ubiquitination
cholesterol metabolic process
anaphase-promoting complex-dependent
proteasomal ubiquitin-dependent protein
catabolic process
response to insulin stimulus
0.617
0.617
0.617
0.614
0.614
0.614
0.613
0.612
0.612
0.612
0.611
0.610
0.609
0.609
0.609
8
56
53
46
67
51
44
12
230
112
7
5
35
83
9
0.609
0.608
95
24
0.607
0.605
0.605
0.605
0.603
0.603
0.603
0.602
0.602
0.601
18
28
22
26
47
42
85
48
10
5
0.601
22
Module name is assigned by the Go Term name, AUC is the performance of the module classifier on Wang dataset
(GSE2034), while the module size is the number of miRNA that regulate the Go Term.
S. Table 3- The Classification performance of ensemble classifier
GSE2034
GSE7390
GSE11121
GSE4922
GSE6532
ACC
0.66
0.64
0.78
0.76
0.73
SN
0.70
0.62
0.83
0.69
0.80
SP
0.65
0.74
0.50
0.88
0.52
AUC
0.73
0.74
0.71
0.69
0.75
MCC
0.29
0.29
0.29
0.24
0.30
The predictive power of the ensemble classifier on the five data sets [1].
S. Table 4 - The classification performance of the Set_median classifier
GSE2034
GSE7390
GSE11121
GSE4922
GSE6532
ACC
0.63
0.65
0.62
0.37
0.65
SN
0.64
0.65
0.60
0.23
0.66
SP
0.61
0.67
0.77
0.87
0.60
AUC
0.68
0.71
0.75
0.63
0.72
MCC
0.25
0.25
0.27
0.07
0.24
The number of the feature at best performance is 196.
S. Table 5 - The classification performance of the Set_centroid classifier
GSE2034
GSE7390
GSE11121
GSE4922
GSE6532
ACC
0.63
0.64
0.61
0.39
0.67
SN
0.64
0.71
0.57
0.27
0.70
SP
0.62
0.62
0.80
0.80
0.58
AUC
0.67
0.71
0.75
0.65
0.71
MCC
0.25
0.26
0.28
0.08
0.25
The number of the feature at best performance is 173.
S. Table 6 - The classification performance of the 70 gene signature classifier
GSE2034
GSE7390
GSE11121
GSE4922
GSE6532
ACC
0.60
0.67
0.77
0.73
0.66
SN
0.64
0.69
0.83
0.85
0.83
SP
0.53
0.57
0.44
0.28
0.09
AUC
0.59
0.64
0.66
0.57
0.47
MCC
0.17
0.21
0.24
0.15
-0.09
The detailed result of the 70 gene classifier on the five NCBI data sets
S. Table 7 - The classification performance of the 76 gene signature classifier
GSE2034
GSE7390
GSE11121
GSE4922
GSE6532
ACC
0.63
0.67
0.44
0.23
0.23
SN
0.76
0.69
0.39
0
0
SP
0.38
0.57
0.71
1
1
AUC
0.57
0.63
0.55
0.5
0.5
MCC
0.14
0.21
0.08
0
0
The detailed result of the 76 gene classifier on the five NCBI data sets
Reference
1.
Zhou X, Liu J, Xiong J: Predicting distant metastasis in breast cancer using ensemble classifier based on
context specific miRNA regulation modules. In: IEEE International Conference on Bioinformatics and
Biomedicine: 2012; Philadelphia. 23-28.