ArticleBMC bioinformatics2017
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Expanding the utility of variant effect predictions with phenotype-specific models.Nature communications · 2025Article
- SOX11 variants cause a neurodevelopmental disorder with infrequent ocular malformations and hypogonadotropic hypogonadism and with distinct DNA methylation profile.Genetics in medicine : official journal of the American College of Medical Genetics · 2022Article
- Classification of user queries according to a hierarchical medical procedure encoding system using an ensemble classifier.Frontiers in artificial intelligence · 2022Article
- Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes.BMC bioinformatics · 2021Article
- Computational Methods for Prediction of Human Protein-Phenotype Associations: A Review.Phenomics (Cham, Switzerland) · 2021Review
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
- Systematic identification of genetic systems associated with phenotypes in patients with rare genomic copy number variations.Human genetics · 2021Article
- Abnormal cranium development in children and adolescents affected by syndromes or diseases associated with neurodysfunction.Scientific reports · 2021Observational
- CellO: comprehensive and hierarchical cell type classification of human cells with the Cell Ontology.iScience · 2021Article
- DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.PLoS computational biology · 2020Article
- ISOGO: Functional annotation of protein-coding splice variants.Scientific reports · 2020Article
- As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018.Yearbook of medical informatics · 2018Review
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4 authors.
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Abstract
backgroundThe prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with human diseases. While the problem of the prediction of gene-disease associations has been widely investigated, the related problem of gene-phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions.
resultsWe present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a "flat" learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity.
conclusionsHierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.
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