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Validation Pipeline for Computational Prediction of Genomics Annotations

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9874))

Abstract

Controlled biomolecular annotations are key concepts in computational genomics and proteomics, since they can describe the functional features of genes and their products in both a simple and computational way. Despite the importance of these annotations, many of them are missing, and the available ones contain errors and inconsistencies; furthermore, the discovery and validation of new annotations are very time-consuming tasks. For these reasons, recently many computer scientists developed several machine-learning algorithms able to computationally predict new gene-function relationships. While several of these methods have been easily adapted from different domains to bioinformatics, their validation remains a challenging aspect of a computational pipeline. Here, we propose a validation procedure based upon three different sub-phases, which is able to assess the precision of any algorithm predictions with a reliable degree of accuracy. We show some validation results obtained for Gene Ontology annotations of Homo sapiens genes that demonstrate the effectiveness of our validation approach.

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Acknowledgments

This work was partially supported by the “Data–Driven Genomic Computing (GenData 2020)” PRIN project (2013–2015), funded by Italy’s Ministry of Education, Universities and Research (MIUR). Authors thank Coby Viner (University of Toronto) for his help in the English proof-reading of this article.

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Authors and Affiliations

  1. Princess Margaret Cancer Centre, University of Toronto, Ontario, Canada

    Davide Chicco

  2. Dipartimento di Elettronica Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

    Marco Masseroli

Authors
  1. Davide Chicco
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  2. Marco Masseroli
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Corresponding author

Correspondence to Davide Chicco .

Editor information

Editors and Affiliations

  1. CNR, Istituto per le Applicazioni del Calcolo, Naples, Italy

    Claudia Angelini

  2. Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milano, Italy

    Paola MV Rancoita

  3. DIBRIS, University of Genoa, Genova, Italy

    Stefano Rovetta

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Chicco, D., Masseroli, M. (2016). Validation Pipeline for Computational Prediction of Genomics Annotations. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-44332-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44331-7

  • Online ISBN: 978-3-319-44332-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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