HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs

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>).

Version:	2.6.1
Depends:	R (≥ 2.10)
Imports:	graph, RBGL, precrec, preprocessCore, methods, plyr, foreach, doParallel, parallel
Suggests:	Rgraphviz, testthat
Published:	2020-03-03
Author:	Marco Notaro [aut, cre], Alessandro Petrini [ctb], Giorgio Valentini [aut]
Maintainer:	Marco Notaro <marco.notaro at unimi.it>
BugReports:	https://github.com/marconotaro/HEMDAG/issues
License:	GPL (≥ 3)
URL:	https://hemdag.readthedocs.io https://github.com/marconotaro/HEMDAG https://anaconda.org/bioconda/r-hemdag
NeedsCompilation:	yes
Citation:	HEMDAG citation info
Materials:	NEWS
CRAN checks:	HEMDAG results

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