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NETWORKX PYTHON MANUAL
Metadata for a reference can be generated like manubot cite -yml doi:10.1016/j.jbi.2011.03.013.Īdding CSL YAML output to media/bibliography.yaml will cache the metadata and allow manual edits in case of errors. Materials for Machine Learning with Ontologies at bio-ontology-research-group/machine-learning-with-ontologies (compilation)īelow are a list of references related to ontology-derived measures of similarity.įeel free to add any reference that provides useful context and details for algorithms supported by this package.Part of the ontologyX suite of R packages. ontologySimilarity mirrored at cran/ontologySimilarity.DiShIn at lasigeBioTM/DiShIn in Python.Semantic Measures Library & ToolKit at sharispe/slib in Java.Here's a list of alternative projects with code for computing semantic similarity measures on ontologies: The package version is automatically generated from the git tag by setuptools_scm. The release action defined by release.yaml will build the distribution and upload to PyPI. # Run all pre-commit checks (CI will also run this). # `git commit` will now trigger automatic checks including linting.
NETWORKX PYTHON INSTALL
Pip install -editable "." # Set up the git pre-commit hooks. Some helpful development commands: # create a virtual environment for development This includes pygraphviz, which requires a pre-existing graphviz installation. The extra viz dependencies are required for the nxontology.viz module. Nxontology can be installed with pip from PyPI like: # standard installation The nxontology-data repository creates NXOntology objects for many popular ontologies / taxonomies. Users can also create their own networkx.DiGraph to use this package. lin, 3 ) 0.699 > # Note that there is also a dedicated reader for the Gene Ontology > from nxontology.imports import read_gene_ontology > read_gene_ontology ( release = "" ) The final line outputs a dictionary like: ) > go_digraph = multidigraph_to_digraph ( go_multidigraph, reduce = True ) > go_nxo = NXOntology ( go_digraph ) > # Notice the similarity increases due to the full set of edges > round ( go_nxo. lin # Access all similarity metrics similarity. similarity ( "gold", "silver", ic_metric = "intrinsic_ic_sanchez" ) # Access a single similarity metric similarity. freeze () # Get object for computing similarity, using the Sanchez et al metric for information content. # Frozen ontologies cache expensive computations. from nxontology.examples import create_metal_nxo metals = create_metal_nxo () # Freezing the ontology prevents adding or removing nodes or edges.
NETWORKX PYTHON HOW TO
Given an NXOntology instance, here how to compute intrinsic similarity metrics. Note that NXOntology represents the ontology as a networkx.DiGraph, where edge direction goes from superterm to subterm. Here, we'll use the example metals ontology: Nxontology is a Python library for representing ontologies using a NetworkX graph.Ĭurrently, the main area of functionality is computing similarity measures between pairs of nodes. NetworkX-based Python library for representing ontologies