Abstract
Functional genomic annotation data banks, which store the associations between genes (or a gene products) and terms of controlled vocabularies describing their features, are paramount in computational biology. Despite their undeniable importance, these data sources cannot be considered neither complete nor totally accurate; in their curated updates often new annotations are added and some of their annotations are revised. In this scenario, computational methods that are able to quicken the curation process of such data banks are very important. To this end, the Latent Semantic Indexing (LSI) by Singular Value Decomposition, and its Semantically IMproved (SIM) variant, have shown to be able to predict novel functional annotations from a set of available ones. In this work, we propose a further improvement of those techniques, based on a preparatory weighting of the associations between genes (or a gene products) and functional annotation terms. We tested the effectiveness of our approach on nine Gene Ontology annotation datasets. The results demonstrated that this technique is able to improve novel annotation predictions.
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Acknowledgments
This research is part of the Search Computing project (2008–2013) funded by the European Research Council (ERC), IDEAS Advanced Grant. The authors would like to thank Luke Lloyd-Jones for the help in the revision of the English style of the text.
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Pinoli, P., Chicco, D., Masseroli, M. (2014). Weighting Scheme Methods for Enhanced Genomic Annotation Prediction. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_6
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DOI: https://doi.org/10.1007/978-3-319-09042-9_6
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