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Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions

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Abstract

The Human Phenotype Ontology (HPO) provides a standard categorization of the phenotypic abnormalities encountered in human diseases and of the semantic relationship between them. Quite surprisingly the problem of the automated prediction of the association between genes and abnormal human phenotypes has been widely overlooked, even if this issue represents an important step toward the characterization of gene-disease associations, especially when no or very limited knowledge is available about the genetic etiology of the disease under study. We present a novel ensemble method able to capture the hierarchical relationships betweenHPO terms, and able to improve existing hierarchical ensemble algorithms by explicitly considering the predictions of the descendant terms of the ontology. In this way the algorithm exploits the information embedded in the most specific ontology terms that closely characterize the phenotypic information associated with each human gene. Genome-wide results obtained by integrating multiple sources of information show the effectiveness of the proposed approach.

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Acknowledgments

We acknowledge partial support from the project “Discovering Patterns in Multi-Dimensional Data” (2016–2017) funded by Università degli Studi di Milano.

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Authors and Affiliations

  1. Anacleto Lab – Dipartimento di Informatica, Università degli Studi di Milano, Via Celoria 18, 20135, Milano, Italy

    Marco Notaro, Marco Frasca, Marco Mesiti & Giorgio Valentini

  2. Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany

    Max Schubach

  3. The Jackson Laboratory for Genomic Medicine, 10 Discovery Dr, Farmington, CT, 06032, USA

    Peter N. Robinson

Authors
  1. Marco Notaro

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  2. Max Schubach

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  3. Marco Frasca

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  4. Marco Mesiti

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  5. Peter N. Robinson

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  6. Giorgio Valentini

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Corresponding author

Correspondence toGiorgio Valentini.

Editor information

Editors and Affiliations

  1. University of Cagliari, Cagliari, Italy

    Massimo Bartoletti

  2. University of Genova, Genoa, Italy

    Annalisa Barla

  3. University of Stirling, Stirling, UK

    Andrea Bracciali

  4. Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

    Gunnar W. Klau

  5. Houston Methodist Research Institute, Houston, TX, USA

    Leif Peterson

  6. University of Udine, Udine, Italy

    Alberto Policriti

  7. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

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Notaro, M., Schubach, M., Frasca, M., Mesiti, M., Robinson, P.N., Valentini, G. (2019). Ensembling Descendant Term Classifiers to Improve Gene - Abnormal Phenotype Predictions. In: Bartoletti, M.,et al. Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2017. Lecture Notes in Computer Science(), vol 10834. Springer, Cham. https://doi.org/10.1007/978-3-030-14160-8_8

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