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HEMDAG package:

• implements several Hierarchical Ensemble Methods (HEMs) for Directed Acyclic Graphs (DAGs);
• reconciles flat predictions with the topology of the ontology;
• can enhance predictions of virtually any flat learning methods by taking into account the hierarchical relationships between ontology classes;
• provides biologically meaningful predictions that obey the true-path-rule, the biological and logical rule that governs the internal coherence of biomedical ontologies;
• 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;
• scales nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples;
• provides several utility functions to process and analyze graphs;
• provides several performance metrics to evaluate HEMs algorithms.

Please get a look to the documentation to know how to download, install and make experiments with the HEMDAG package.

If you use HEMDAG, please cite our Bioinformatics article or BMC Bioinformatics article:

Marco Notaro, Marco Frasca, Alessandro Petrini, Jessica Gliozzo, Elena Casiraghi, Peter N Robinson, Giorgio Valentini
HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve Gene Ontology term prediction,
Bioinformatics, Volume 37, Issue 23, 1 December 2021, Pages 4526–4533

M. Notaro, M. Schubach, P. N. Robinson, and G Valentini.
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.
BMC Bioinformatics, 18(1):449, 2017