From: Mining physical protein-protein interactions from the literature
Model | Precision | Recall | F1 score | AUC |
---|---|---|---|---|
Mean | 0.6642 | 0.7636 | 0.6868 | 0.7351 |
Standard deviation | 0.0810 | 0.1926 | 0.1035 | 0.0741 |
Best reported in terms of AUC [8] | 0.7080 | 0.8609 | 0.7770 | 0.8554 |
Our results in BioCreative 2006 | 0.7507 | 0.8107 | 0.7795 | 0.8471 |
Term (baseline) | 0.7016 | 0.8213 | 0.7568 | 0.8037 |
String | 0.7044 | 0.8960 | 0.7887 | 0.8416 |
Named entity (NE) | 0.5815 | 0.9600 | 0.7243 | 0.7570 |
Template | 0.7841 | 0.7653 | 0.7746 | 0.8239 |
String + NE | 0.7360 | 0.8773 | 0.8005 | 0.8479 |
String + template | 0.7416 | 0.8880 | 0.8082 | 0.8372 |
String + NE + template | 0.7585 | 0.8373 | 0.7959 | 0.8507 |
String + term + NE + template | 0.7432 | 0.8720 | 0.8025 | 0.8608 |
Naïve Bayes classifier | 0.6321 | 0.8613 | 0.7291 | 0.7884 |
Multinomial classifier | 0.6264 | 0.8720 | 0.7290 | 0.7770 |
Linear kernel SVM | 0.7016 | 0.8213 | 0.7568 | 0.8037 |
p-spectrum kernel SVM (p = 7) | 0.7352 | 0.8293 | 0.7794 | 0.8376 |
Integration of the above four classifiers (AdaBoost) | 0.7995 | 0.8933 | 0.8438 | 0.8746 |