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HEMDAG (version 2.1.3)

Hierarchical Ensemble Methods for Directed Acyclic Graphs

Description

An implementation of Hierarchical Ensemble Methods for Directed Acyclic Graphs (DAGs). The 'HEMDAG' package can be used to enhance the predictions of virtually any flat learning methods, by taking into account the hierarchical nature of the classes of a bio-ontology. 'HEMDAG' 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 it can be also safely applied to tree-structured taxonomies (as FunCat), since trees are DAGs. 'HEMDAG' scale nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) ).

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Version

Install

install.packages('HEMDAG')

Monthly Downloads

681

Version

2.1.3

License

GPL (>= 3)

Maintainer

Marco Notaro

Last Published

May 21st, 2018

Functions in HEMDAG (2.1.3)

Multilabel.F.measure

Multilabel F-measure
check.DAG.integrity

DAG checker
do.subgraph

Build subgraph
hierarchical.checkers

Hierarchical constraints cheker
root.node

Root node
normalize.max

Max normalization
read.undirected.graph

Read an undirected graph from a file
transitive.closure.annotations

Transitive closure of annotations
tupla.matrix

Tupla Matrix
check.annotation.matrix.integrity

Annotation matrix checker
do.submatrix

Build submatrix
weighted.adjacency.matrix

Weighted Adjacency Matrix
children

Build children
find.best.f

Best hierarchical F-score
do.unstratified.cv.data

Unstratified cross-validation
compute.flipped.graph

Flip Graph
find.leaves

Leaves
scores.normalization

Scores Normalization Function
example.datasets

Small real example datasets
write.graph

Write a directed graph on file
constraints.matrix

Constraints Matrix
specific.annotation.list

Specific annotations list
TPR-DAG-variants

TPR-DAG Variants
full.annotation.matrix

Full annotations matrix
descendants

Build descendants
graph.levels

Build Graph Levels
read.graph

Read a directed graph from a file
parents

Build parents
distances.from.leaves

Distances from leaves
do.edges.from.HPO.obo

Parse an HPO OBO file
specific.annotation.matrix

HPO specific annotations matrix
stratified.cross.validation

Stratified cross validation
Do.HTD.holdout

HTD-DAG holdout
Do.flat.scores.normalization

Flat scores normalization
DESCENS

DESCENS variants
Do.HTD

HTD-DAG vanilla
TPR-DAG

TPR-DAG variants
ancestors

Build ancestors
Do.full.annotation.matrix

Do full annotations matrix
Do.heuristic.methods.holdout

Do Heuristic Methods holdout
Do.heuristic.methods

Do Heuristic Methods
FMM

Compute Multilabel F-measure
PXR

Precision at fixed Recall level
TPR-DAG-cross-validation

TPR-DAG cross-validation experiments
HEMDAG-package

HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs
Heuristic-Methods

Obozinski Heuristic Methods
AUPRC

AUPRC measures
HTD-DAG

HTD-DAG
AUROC

AUROC measures
TPR-DAG-holdout

TPR-DAG holdout experiments