Motivation: Computational methods play a central role in annotating the functions of large amounts of proteins delivered by high-throughput technologies. Despite the encouraging results achieved by these methods, many functions still have a very low number of verified protein annotations, leading to a pronounced imbalance between annotated and unannotated proteins. Furthermore, functional taxonomies rarely report negative annotations. This leaves ill defined the set of negative examples, which is crucial for training the majority of machine learning methods. In practice, neglecting data imbalance and the problemof selecing negative examples can strongly limit the accuracy of protein function prediction. Results: We present a novel approach combining a suitable imbalance-aware classification strategy, addressing the scarcity of annotated proteins, with an active learning strategy for selecting the most reliable negative examples . 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
Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels. The learning algorithm must thus select which unlabeled examples to use as negative training points, possibly ending up with an unbalanced learning problem . 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.
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Motivation: Computational methods play a central role in annotating the functions of large amounts of proteins delivered by high-throughput technologies. Despite the encouraging results achieved by these methods, many functions still have a very low number of verified protein annotations, leading to a pronounced imbalance between annotated and unannotated proteins. Furthermore, functional taxonomies rarely report negative annotations. This leaves ill defined the set of negative examples, which is crucial for training the majority of machine learning methods. In practice, neglecting data imbalance and the problemof selecing negative examples can strongly limit the accuracy of protein function prediction. Results: We present a novel approach combining a suitable imbalance-aware classification strategy, addressing the scarcity of annotated proteins, with an active learning strategy for selecting the most reliable negative examples
Motivated by applications in protein function prediction, we consider a challenging supervised classification setting in which positive labels are scarce and there are no explicit negative labels. The learning algorithm must thus select which unlabeled examples to use as negative training points, possibly ending up with an unbalanced learning problem
R
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
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.