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Extended Spearman and Kendall Coefficients for Gene Annotation List Correlation

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2014)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8623))

Abstract

Gene annotations are a key concept in bioinformatics and computational methods able to predict them are a fundamental contribution to the field. Several machine learning algorithms are available in this domain; they include relevant parameters that might influence the output list of predicted gene annotations. The amount that the variation of these key parameters affect the output gene annotation lists remains an open aspect to be evaluated. Here, we provide support for such evaluation by introducing two list correlation measures; they are based on and extend the Spearman ρ correlation coefficient and Kendall τ distance, respectively. The application of these measures to some gene annotation lists, predicted from Gene Ontology annotation datasets of different organisms’ genes, showed interesting patterns between the predicted lists. Additionally, they allowed expressing some useful considerations about the prediction parameters and algorithms used.

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Author information

Authors and Affiliations

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

    Davide Chicco, Eleonora Ciceri & Marco Masseroli

  2. Princess Margaret Cancer Centre, University of Toronto, Toronto, Canada

    Davide Chicco

Authors
  1. Davide Chicco
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  2. Eleonora Ciceri
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  3. Marco Masseroli
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Corresponding author

Correspondence to Davide Chicco .

Editor information

Editors and Affiliations

  1. CUSSB, University "Vita-Salute" San Raffae, Milano, Italy

    Clelia DI Serio

  2. The Computer Laboratory, University of Cambridge, Cambridge, United Kingdom

    Pietro Liò

  3. CUSSB, Università Vita-Salute San Raffaele, Milano, Italy

    Alessandro Nonis

  4. Dipartimento di Informatica, Universitá degli Studi di Salerno, Fisciano, Salerno, Italy

    Roberto Tagliaferri

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Chicco, D., Ciceri, E., Masseroli, M. (2015). Extended Spearman and Kendall Coefficients for Gene Annotation List Correlation. In: DI Serio, C., Liò, P., Nonis, A., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2014. Lecture Notes in Computer Science(), vol 8623. Springer, Cham. https://doi.org/10.1007/978-3-319-24462-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-24462-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24461-7

  • Online ISBN: 978-3-319-24462-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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