Before revision:  When implemented in a Support Vector Machine, this combined approach shows improved accuracy on yeast and human proteomes over standard SVM and top-performing function prediction tools
After revision:  We address these issues by proposing an algorithm that combines active learning (for selecting negative examples) with imbalance-aware learning (for mitigating the label imbalance). In our experiments we observe that these two techniques operate synergistically, outperforming state-of-the-art methods on standard protein function prediction benchmarks.