Prediction of Human Gene - Phenotype Associations by Exploiting the Hierarchical Structure of the Human Phenotype Ontology

  • Giorgio Valentini
  • Sebastian Köhler
  • Matteo Re
  • Marco Notaro
  • Peter N. Robinson
Conference paper

DOI: 10.1007/978-3-319-16483-0_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9043)
Cite this paper as:
Valentini G., Köhler S., Re M., Notaro M., Robinson P.N. (2015) Prediction of Human Gene - Phenotype Associations by Exploiting the Hierarchical Structure of the Human Phenotype Ontology. In: Ortuño F., Rojas I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2015. Lecture Notes in Computer Science, vol 9043. Springer, Cham

Abstract

The Human Phenotype Ontology (HPO) provides a conceptualization of phenotype information and a tool for the computational analysis of human diseases. It covers a wide range of phenotypic abnormalities encountered in human diseases and its terms (classes) are structured according to a directed acyclic graph. In this context the prediction of the phenotypic abnormalities associated to human genes is a key tool to stratify patients into disease subclasses that share a common biological or pathophisiological basis. Methods are being developed to predict the HPO terms that are associated for a given disease or disease gene, but most such methods adopt a simple ”flat” approach, that is they do not take into account the hierarchical relationships of the HPO, thus loosing important a priori information about HPO terms. In this contribution we propose a novel Hierarchical Top-Down (HTD) algorithm that associates a specific learner to each HPO term and then corrects the predictions according to the hierarchical structure of the underlying DAG. Genome-wide experimental results relative to a complex HPO DAG including more than 4000 HPO terms show that the proposed hierarchical-aware approach significantly improves predictions obtained with flat methods, especially in terms of precision/recall results.

Keywords

Human Phenotype Ontology term prediction Ensemble methods Hierarchical classification methods Disease gene prioritization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giorgio Valentini
    • 1
  • Sebastian Köhler
    • 2
  • Matteo Re
    • 1
  • Marco Notaro
    • 3
  • Peter N. Robinson
    • 2
    • 4
  1. 1.AnacletoLab - DI, Dipartimento di InformaticaUniversità degli Studi di MilanoItaly
  2. 2.Institut fur Medizinische Genetik und HumangenetikCharité - Universitatsmedizin BerlinGermany
  3. 3.Dipartimento di BioscienzeUniversità degli Studi di MilanoItaly
  4. 4.Institute of Bioinformatics, Department of Mathematics and Computer ScienceFreie Universitat BerlinGermany

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