An implementation of several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs). 'HEMDAG' package: 1) reconciles flat predictions with the topology of the ontology; 2) can enhance the predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes; 3) provides biologically meaningful predictions that always obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies; 4) is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but can be safely applied to tree-structured taxonomies as well (as FunCat), since trees are DAGs; 5) scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples; 6) provides several utility functions to process and analyze graphs; 7) provides several performance metrics to evaluate HEMs algorithms. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
|Author||Marco Notaro [aut, cre] (<https://orcid.org/0000-0003-4309-2200>), Alessandro Petrini [ctb] (<https://orcid.org/0000-0002-0587-1484>), Giorgio Valentini [aut] (<https://orcid.org/0000-0002-5694-3919>)|
|Maintainer||Marco Notaro <[email protected]>|
|License||GPL (>= 3)|
|URL||https://hemdag-tutorials.readthedocs.io https://github.com/marconotaro/HEMDAG https://anaconda.org/bioconda/r-hemdag|
|Package repository||View on GitHub|
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