Other Examples¶
The following examples will assume the default options, i.e. the values shown are calculated using extrinsic IC and DCA.
Gene Ontology (GO) and UniProt proteins¶
Download the latest version of the database we created:
wget http://labs.rd.ciencias.ulisboa.pt/dishin/go202104.db.gz
gunzip -N go202104.db.gz
Now to calculate the similarity between maltose biosynthetic process and maltose catabolic process, first we need to obtain the semantic base IDs of those concepts:
ssmpy.semantic_base("go.db")
e1 = ssmpy.get_id("GO_0000023")
e2 = ssmpy.get_id("GO_0000025")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
4.315813746201754
0.38793452313030363
0.06840605034663635
Now to calculate the similarity between proteins Q12345 and Q12346, first we retrieve the GO terms associated with each one:
e1 = ssmpy.get_uniprot_annotations("Q12345")
e2 = ssmpy.get_uniprot_annotations("Q12346")
Next we use the ssm_multiple to calculate the average maximum semantic similarity, using the resnik measure
ssmpy.ssm_multiple(ssmpy.ssm_resnik, e1, e2)
ssmpy.ssm_multiple(ssmpy.ssm_lin, e1, e2)
ssmpy.ssm_multiple(ssmpy.ssm_jiang_conrath, e1, e2)
Output:
0.6015115682274214
0.12201023476842265
0.09317326288224918
To create an updated version of the database, 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
The annotations will be used to calculate the extrinsic information content.
Next create the semantic base:
ssmpy.create_semantic_base("go.owl", "go.db", "http://purl.obolibrary.org/obo/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", "goa_uniprot_all_noiea.gaf)
This is stored in the form of a sqlite database on the same directory of your project.
Chemical Entities of Biological Interest (ChEBI) Example¶
Download the lastest version of the database we created:
wget http://labs.rd.ciencias.ulisboa.pt/dishin/chebi202104.db.gz
gunzip -N chebi202104.db.gz
Now to calculate the similarity between aripiprazole and bithionol execute:
ssmpy.semantic_base("chebi.db")
e1 = ssmpy.get_id("CHEBI_31236")
e2 = ssmpy.get_id("CHEBI_3131")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
1.4393842298350599
0.12935491517581163
0.049077257018319796
To create an updated version of the database, download the ontology:
wget http://purl.obolibrary.org/obo/chebi/chebi_lite.owl
And then create the new database:
ssmpy.create_semantic_base("chebi_lite.owl", "chebi.db", "http://purl.obolibrary.org/obo/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", '')
Human Phenotype (HP) Example¶
Download the lastest version of the database we created:
wget http://labs.rd.ciencias.ulisboa.pt/dishin/hp202104.db.gz
gunzip -N hp202104.db.gz
Now to calculate the similarity between Optic nerve coloboma and Optic nerve dysplasia execute:
ssmpy.semantic_base("hp.db")
e1 = ssmpy.get_id("HP_0000588")
e2 = ssmpy.get_id("HP_0001093")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
4.593979372426621
0.5118244533189668
0.10242304162282165
To create an updated version of the database, download the ontology:
wget http://purl.obolibrary.org/obo/hp.owl
And then create the new database:
ssmpy.create_semantic_base("hp.owl", "hp.db", "http://purl.obolibrary.org/obo/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", '')
Human Disease Ontology (HDO) Example¶
Download the lastest version of the database we created:
wget http://labs.rd.ciencias.ulisboa.pt/dishin/doid202104.db.gz
gunzip -N doid202104.db.gz
Now to calculate the similarity between Asthma and Lung cancer execute:
ssmpy.semantic_base("doid.db")
e1 = ssmpy.get_id("DOID_2841")
e2 = ssmpy.get_id("DOID_1324")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
2.3627836143597176
0.4328907089097581
0.13906777879867938
To create an updated version of the database, download the ontology:
wget http://purl.obolibrary.org/obo/doid.owl
And then create the new database:
ssmpy.create_semantic_base("doid.owl", "doid.db", "http://purl.obolibrary.org/obo/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", '')
Medical Subject Headings (MeSH) Example¶
Download the lastest version of the database we created:
wget http://labs.rd.ciencias.ulisboa.pt/dishin/mesh202104.db.gz
gunzip -N mesh202104.db.gz
Now to calculate the similarity between Malignant Hyperthermia and Fever execute:
ssmpy.semantic_base("mesh.db")
e1 = ssmpy.get_id("D008305")
e2 = ssmpy.get_id("D005334")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
1.2582571367910345
0.17390901691859173
0.07719755683816652
To create an updated version of the database, download the _NT_ version from ftp://nlmpubs.nlm.nih.gov/online/mesh/rdf/mesh.nt.gz and unzip it:
wget ftp://nlmpubs.nlm.nih.gov/online/mesh/rdf/mesh.nt.gz
gunzip mesh.nt.gz
And then create the new database:
ssmpy.create_semantic_base("mesh.nt", "mesh.db", "http://id.nlm.nih.gov/mesh/", "http://id.nlm.nih.gov/mesh/vocab#broaderDescriptor", '')
Radiology Lexicon (RadLex) Example¶
Download the lastest version of the database we created:
wget http://labs.rd.ciencias.ulisboa.pt/dishin/radlex202104.db.gz
gunzip -N radlex202104.db.gz
Now to calculate the similarity between nervous system of right upper limb and nervous system of left upper limb execute:
ssmpy.semantic_base("radlex.db")
e1 = ssmpy.get_id("RID16139")
e2 = ssmpy.get_id("RID16140")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
9.366531825151093
0.9310964912333252
0.41905978419640516
To create an updated version of the database, download the RDF/XML version from http://bioportal.bioontology.org/ontologies/RADLEX and save it as radlex.rdf
And then create the new database:
ssmpy.create_semantic_base("radlex.rdf", "radlex.db", "http://www.radlex.org/RID/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", '')
WordNet Example¶
Download the lastest version of the database we created:
wget http://labs.rd.ciencias.ulisboa.pt/dishin/wordnet202104.db.gz
gunzip -N wordnet202104.db.gz
Now to calculate the similarity between the nouns ambulance and motorcycle execute:
ssmpy.semantic_base("wordnet.db")
e1 = ssmpy.get_id("ambulance-noun-1")
e2 = ssmpy.get_id("motorcycle-noun-1")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
6.331085809208157
0.6792379292396559
0.14327549414725688
To create an updated version of the database, download the ontology:
wget http://www.w3.org/2006/03/wn/wn20/rdf/wordnet-hyponym.rdf
And then create the new database:
ssmpy.create_semantic_base("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", '')