Basic functions to start using semantic similarity measures directly from a rdf or owl file.
Project description
DiShIn: Semantic Similarity Measures using Disjunctive Shared Information
This software package provides the basic functions to start using semantic similarity measures directly from a rdf or owl file.
A web tool using this package is available at: http://labs.fc.ul.pt/dishin/
INSTALLATION
Either clone this repository or install from pypi:
pip install ssmpy
USAGE:
python dishin.py <semanticbase>.db <term1> <term2>
python dishin.py <semanticbase>.[owl|rdf] <semanticbase>.db <name_prefix> <relation> <annotation_file>
or use the python functions directly
>>> import ssmpy
Metals Example
To create the semantic base file (metals.db) from the metals.owl file:
python dishin.py metals.owl metals.db https://raw.githubusercontent.com/lasigeBioTM/ssm/master/metals.owl# http://www.w3.org/2000/01/rdf-schema#subClassOf metals.txt
The metals.txt contains the a list of occurrences. For example, the following contents has one occurrence for each term, except gold and silver with two occurrences.
gold
silver
gold
silver
copper
platinum
palladium
metal
coinage
precious
Now to calculate the similarity between copper and gold execute:
python dishin.py metals.db copper gold
Output:
Resnik DiShIn intrinsic 0.293893332451
Resnik MICA intrinsic 0.587786664902
Lin DiShIn intrinsic 0.195397745542
Lin MICA intrinsic 0.390795491084
JC DiShIn intrinsic 0.413160290851
JC MICA intrinsic 0.545678333969
Resnik DiShIn extrinsic 0.225992561872
Resnik MICA extrinsic 0.451985123743
Lin DiShIn extrinsic 0.15045953662
Lin MICA extrinsic 0.30091907324
JC DiShIn extrinsic 0.391842474063
JC MICA extrinsic 0.476176683193
Using the python function directly (first download metals.db and metals.txt from this repository):
>>> ssmpy.create_semantic_base("metals.owl", "metals.db", "https://raw.githubusercontent.com/lasigeBioTM/ssm/master/metals.owl#", "http://www.w3.org/2000/01/rdf-schema#subClassOf", "metals.txt")
>>> ssmpy.semantic_base("metals.db")
>>> e1 = ssmpy.get_id("copper")
>>> e2 = ssmpy.get_id("gold")
>>> ssmpy.ssm_resnik (e1,e2)
Gene Ontology (GO) and UniProt proteins Example
Download the ontology and annotations:
wget http://purl.obolibrary.org/obo/go.owl
wget http://geneontology.org/gene-associations/goa_uniprot_all_noiea.gaf.gz
gunzip goa_uniprot_all_noiea.gaf.gz
Create the semantic base:
python dishin.py go.owl go.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf goa_uniprot_all_noiea.gaf
Now to calculate the similarity between maltose biosynthetic process and maltose catabolic process execute:
python dishin.py go.db GO_0000023 GO_0000025
Output:
Resnik DiShIn intrinsic 3.77628827887
Resnik MICA intrinsic 8.90977567369
Lin DiShIn intrinsic 0.407967349889
Lin MICA intrinsic 0.962558285087
JC DiShIn intrinsic 0.0912398605342
JC MICA intrinsic 1.44269504089
Resnik DiShIn extrinsic 3.90322051564
Resnik MICA extrinsic 10.8429770989
Lin DiShIn extrinsic 0.335106659477
Lin MICA extrinsic 0.930911748348
JC DiShIn extrinsic 0.0645621511038
JC MICA extrinsic 0.62133493456
Now to calculate the similarity between proteins Q12345 and Q12346 execute:
python dishin.py go.db Q12345 Q12346
Output:
Resnik DiShIn intrinsic 0.71245987296
Resnik MICA intrinsic 0.86114182192
Lin DiShIn intrinsic 0.0815862437434
Lin MICA intrinsic 0.0986072045826
JC DiShIn intrinsic 0.0746193683317
JC MICA intrinsic 0.0747498775484
Resnik DiShIn extrinsic 0.263180891661
Resnik MICA extrinsic 0.479787642163
Lin DiShIn extrinsic 0.0407370976359
Lin MICA extrinsic 0.0744385765602
JC DiShIn extrinsic 0.0963202202036
JC MICA extrinsic 0.0977061369628
Chemical Entities of Biological Interest (ChEBI) Example
Download the ontology:
wget ftp://ftp.ebi.ac.uk/pub/databases/chebi/ontology/chebi_lite.owl
Create the semantic base:
python dishin.py chebi_lite.owl chebi.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''
Now to calculate the similarity between aripiprazole and bithionol execute:
python dishin.py chebi.db CHEBI_31236 CHEBI_3131
Output:
Resnik DiShIn intrinsic 1.35348538334
Resnik MICA intrinsic 5.36203900206
Lin DiShIn intrinsic 0.124157709369
Lin MICA intrinsic 0.491869722596
JC DiShIn intrinsic 0.0523677861211
JC MICA intrinsic 0.0902640991714
Human Phenotype (HP) Example
Download the ontology:
wget http://purl.obolibrary.org/obo/hp.owl
Create the semantic base:
python dishin.py hp.owl hp.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''
Now to calculate the similarity between Optic nerve coloboma and Optic nerve dysplasia execute:
python dishin.py hp.db HP_0000588 HP_0001093
Output:
Resnik DiShIn intrinsic 3.05120683059
Resnik MICA intrinsic 6.08995149382
Lin DiShIn intrinsic 0.346806801862
Lin MICA intrinsic 0.692197126688
JC DiShIn intrinsic 0.0870050196333
JC MICA intrinsic 0.184634686534
Human Disease Ontology (HDO) Example
Download the ontology:
wget https://raw.githubusercontent.com/DiseaseOntology/HumanDiseaseOntology/master/src/ontology/doid.owl
Create the semantic base:
python dishin.py doid.owl doid.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''
Now to calculate the similarity between Asthma and Lung cancer execute:
python dishin.py doid.db DOID_2841 DOID_1324
Output:
Resnik DiShIn intrinsic 2.29931853312
Resnik MICA intrinsic 3.70394398358
Lin DiShIn intrinsic 0.409564492804
Lin MICA intrinsic 0.659762410974
JC DiShIn intrinsic 0.150841449132
JC MICA intrinsic 0.261764573924
Radiology Lexicon (RadLex) Example
Download the RDF/XML version from http://bioportal.bioontology.org/ontologies/RADLEX and save it as radlex.rdf
Create the semantic base:
python dishin.py radlex.rdf radlex.db http://www.radlex.org/RID/# http://www.radlex.org/RID/#Is_A ''
Now to calculate the similarity between nervous system of right upper limb and nervous system of left upper limb execute:
python dishin.py radlex.db RID16139 RID16140
Output:
Resnik MICA intrinsic 9.35897587112
Lin MICA intrinsic 0.931044698021
JC MICA intrinsic 0.721347520444
WordNet Example
Download the ontology:
wget http://www.w3.org/2006/03/wn/wn20/rdf/wordnet-hyponym.rdf
Create the semantic base:
python dishin.py wordnet-hyponym.rdf wordnet.db http://www.w3.org/2006/03/wn/wn20/instances/synset- http://www.w3.org/2006/03/wn/wn20/schema/hyponymOf ''
Now to calculate the similarity between the nouns ambulance and motorcycle execute:
python dishin.py wordnet.db ambulance-noun-1 motorcycle-noun-1
Output:
Resnik MICA intrinsic 6.33108580921
Lin MICA intrinsic 0.67923792924
JC MICA intrinsic 0.167236367313
Data Sources
Gene Ontology (GO)
- Ontology: http://geneontology.org/page/download-ontology#go.obo_and_go.owl;
- Annotation files (extrinsic): http://www.geneontology.org/page/download-annotations
- SemanticBase: http://labs.rd.ciencias.ulisboa.pt/dishin/go.db
ChEBI
- Ontology: ftp://ftp.ebi.ac.uk/pub/databases/chebi/ontology/
- SemanticBase: http://labs.rd.ciencias.ulisboa.pt/dishin/chebi.db
Human Phenotype ontology (HPO)
- Ontology: http://human-phenotype-ontology.github.io/downloads.html
- SemanticBase: http://labs.rd.ciencias.ulisboa.pt/dishin/hp.db
Human Disease Ontology (DO)
- Ontology: https://github.com/DiseaseOntology/HumanDiseaseOntology/tree/master/src/ontology
- SemanticBase: http://labs.rd.ciencias.ulisboa.pt/dishin/doid.db
RadLex
- Ontology: http://bioportal.bioontology.org/ontologies/RADLEX
- SemanticBase: http://labs.rd.ciencias.ulisboa.pt/dishin/radlex.db
WordNet
- Ontology: http://www.w3.org/2006/03/wn/wn20/rdf/wordnet-hyponym.rdf
- SemanticBase: http://labs.rd.ciencias.ulisboa.pt/dishin/wordnet.db
Source Code
-
semanticbase.py : provides a function to produce the semantic-base as a SQLite database
-
ssm.py : provides the functions to calculate semantic similarity based on the SQLite database
-
annotations.py : provides the functions to get the annotations for the given proteins
-
dishin.py : executes the functions according to the input given
Reference:
- F. Couto and A. Lamurias, “Semantic similarity definition,” in Encyclopedia of Bioinformatics and Computational Biology (S. Ranganathan, K. Nakai, C. Schönbach, and M. Gribskov, eds.), vol. 1, pp. 870–876, Oxford: Elsevier, 2019 [https://doi.org/10.1016/B978-0-12-809633-8.20401-9] [https://www.researchgate.net/publication/323219905_Semantic_Similarity_Definition]
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