Abstract
The Automated Functional Prediction (AFP) of proteins became a challenging problem in bioinformatics and biomedicine aiming at handling and interpreting the extremely large-sized proteomes of several eukaryotic organisms. A central issue in AFP is the absence in public repositories for protein functions, e.g. the Gene Ontology (GO), of well defined sets of negative examples to learn accurate classifiers for AFP. In this paper we investigate the Query by Committee paradigm of active learning to select the negatives most informative for the classifier and the protein function to be inferred. We validated our approach in predicting the Gene Ontology function for the S.cerevisiae proteins.
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Notes
- 1.
A balanced seed training set counterbalances the predominance of 0 labels.
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Acknowledgments
This work was supported by the grant title Machine learning algorithms to handle label imbalance in biomedical taxonomies, code PSR2017\(\_\)DIP\(\_\)010\(\_\)MFRAS, Università degli Studi di Milano.
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Frasca, M., Sepehri, M., Petrini, A., Grossi, G., Valentini, G. (2020). Committee-Based Active Learning to Select Negative Examples for Predicting Protein Functions. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_7
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