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Table 6 Runs submitted for IMT

From: Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions

Run number Label Algorithm Number of features
1 j48-21 J48 All (21 features)
2 rc-21 Random Committee All (21 features)
3 rf-21 Random Forest All (21 features)
4 j48-14 J48 14 features
5 rf-12 Random Forest 12 features
6 rc-12 Random Committee 12 features
7 rc-14 Random Committee 14 features
8 rf-7 Random Forest 7 features
9 nbt-7 Naïve Bayes Tree 7 features
10 rf-15 Random Forest 15 features
  1. For the BioCreative III challenge, each participating team was allowed to submit 10 runs for IMT. Five runs could be submitted offline and the other five runs could be submitted online, using XML-RPC. Runs 1-5 were submitted offline, while runs 6-10 were submitted online. For all runs, we combined the training and the development data. We submitted 10 runs, listed here.