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.
Prediction of human gene - phenotype associations by exploiting the hierarchical structure of the human phenotype ontology / G. Valentini, S. Köhler, M. Re, M. Notaro, P.N. Robinson (LECTURE NOTES IN COMPUTER SCIENCE). - In: Bioinformatics and biomedical engineering : third international conference, IWBBIO 2015, Granada, Spain, April 15-17, 2015 : proceedings / [a cura di] F. Ortuño, I. Rojas. - Cham : Springer, 2015. - ISBN 9783319164823. - pp. 66-77 (( Intervento presentato al 3. convegno International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) tenutosi a Granada nel 2015 [10.1007/978-3-319-16483-0_7].
Prediction of human gene - phenotype associations by exploiting the hierarchical structure of the human phenotype ontology
G. ValentiniPrimo
;M. Re;M. Notaro;
2015
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.File | Dimensione | Formato | |
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