Hierarchical Ensemble Methods for Directed Acyclic Graphs
Binary upper triangular adjacency matrix
AUPRC measures
AUROC measures
Build ancestors
Build children
Build consistent graph
Build descendants
Parse an HPO obo file
Build parents
Build scores matrix
Build subgraph
Build submatrix
Annotation matrix checker
DAG checker
Flip graph
Constraints matrix
DataFrame for stratified cross validation
Distances from leaves
Small real example datasets
Best hierarchical F-score
Leaves
Compute Fmax
Full annotation matrix
GPAV holdout
GPAV over examples
GPAV over examples -- parallel implementation
Generalized Pool-Adjacent Violators (GPAV)
GPAV vanilla
Build graph levels
HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs
Hierarchical constraints checker
HTD-DAG holdout
HTD-DAG
HTD-DAG vanilla
Lexicographical topological sorting
multilabel F-measure
Max normalization
Obozinski heuristic methods
Obozinski's heuristic methods -- holdout
Obozinski's heuristic methods calling
Precision-Recall curves
Read a directed graph from a file
Read an undirected graph from a file
Root node
Scores normalization function
Specific annotations list
Specific annotation matrix
Stratified cross validation
TPR-DAG cross-validation experiments
TPR-DAG holdout experiments
TPR-DAG ensemble variants
Transitive closure of annotations
Tupla matrix
Unstratified cross validation
Weighted adjacency matrix
Write a directed graph on file
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>).