Table 9 |
||||||
|
ACT feature knock-out experiments for SVM |
||||||
|
Features |
F1 score |
Specificity |
Sensitivity |
Accuracy |
Matthews Coef |
AUC iP/R |
|
|
||||||
|
B |
73.45 |
93.02 |
67.95 |
85.75 |
64.19 |
72.44 |
|
N |
31.75 |
98.07 |
19.76 |
75.35 |
31.50 |
42.04 |
|
C |
69.47 |
93.58 |
61.58 |
84.30 |
60.03 |
69.98 |
|
M |
69.07 |
91.63 |
63.56 |
83.49 |
58.33 |
68.33 |
|
|
||||||
|
BC |
74.93 |
94.10 |
68.55 |
86.69 |
66.50 |
73.92 |
|
BCM |
76.71 |
94.33 |
70.86 |
87.52 |
68.70 |
76.00 |
|
BNCM |
77.01 |
94.48 |
71.08 |
87.69 |
69.14 |
76.22 |
|
|
||||||
|
Results of feature knock-out experiments on the combined ACT training and development datasets (%) with Support Vector Machine (SVM). B – bag of words; N – named entities; C – contextual words surrounding proteins; M – MeSH descriptors. |
||||||
|
Wang et al. BMC Bioinformatics 2011 12(Suppl 8):S11 doi:10.1186/1471-2105-12-S8-S11 |
||||||