The HEMDAG package:
provides an implementation of 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;
A comprehensive tutorial showing how to apply HEMDAG to real case bio-medical case studies is available at https://hemdag.readthedocs.io.
The HEMDAG package implements the following Hierarchical Ensemble Methods for DAGs:
HTD-DAG: Hierarchical Top Down (htd);
GPAV-DAG: Generalized Pool-Adjacent Violators, Burdakov et al. (gpav);
TPR-DAG: True-Path Rule (tpr.dag);
DESCENS: Descendants Ensemble Classifier (tpr.dag);
ISO-TPR: Isotonic-True-Path Rule (tpr.dag);
Max, And, Or: Heuristic Methods, Obozinski et al. (obozinski.heuristic.methods);
Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini, Prediction of Human Phenotype Ontology terms by means of Hierarchical Ensemble methods, BMC Bioinformatics 2017, 18(1):449, 10.1186/s12859-017-1854-y