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:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/go202302.db.gz
gunzip -N go202302.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:
4.310175268311233
0.39394227804683757
0.07011679859284012
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:
0.47838902966531843
0.09255485176480098
0.09036345699860157
To create an updated version of the database, download the ontology and annotations:
curl -L -O http://purl.obolibrary.org/obo/go.owl
curl -L -O 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:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/chebi202302.db.gz
gunzip -N chebi202302.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:
1.476632415671022
0.13064609500712546
0.048421904489541145
To create an updated version of the database, download the ontology:
curl -L -O 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:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/hp202302.db.gz
gunzip -N hp202302.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:
4.617876786673204
0.5108021406767812
0.10157307132754399
To create an updated version of the database, download the ontology:
curl -L -O 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:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/doid202302.db.gz
gunzip -N doid202302.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:
2.4006601711893345
0.4363310773119593
0.13884016970564805
To create an updated version of the database, download the ontology:
curl -L -O 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", '')
Ontology for Stem Cell Investigations (OSCI)¶
Download the lastest version of the database we created:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/osci202302.db.gz
gunzip -N osci202302.db.gz
Now to calculate the similarity between neuronal stem cell and sensory neuron execute:
ssmpy.semantic_base("osci.db")
e1 = ssmpy.get_id("CL_0000047")
e2 = ssmpy.get_id("CL_0000101")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
3.1560550137337486
0.6369806275261506
0.21751839842873807
To create an updated version of the database, download the ontology:
curl -L -O https://raw.githubusercontent.com/stemcellontologyresource/OSCI/master/src/ontology/osci.owl
And then create the new database:
ssmpy.create_semantic_base("osci.owl", "osci.db", "http://purl.obolibrary.org/obo/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", '')
Cell Ontology (CL)¶
Download the lastest version of the database we created:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/cl202302.db.gz
gunzip -N cl202302.db.gz
Now to calculate the similarity between neuronal stem cell and sensory neuron execute:
ssmpy.semantic_base("cl.db")
e1 = ssmpy.get_id("CL_0000047")
e2 = ssmpy.get_id("CL_0000101")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
1.9063375764548978
0.3272258822885516
0.11313710298029131
To create an updated version of the database, download the ontology:
curl -L -O http://purl.obolibrary.org/obo/cl.owl
And then create the new database:
ssmpy.create_semantic_base("cl.owl", "cl.db", "http://purl.obolibrary.org/obo/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", '')
Environmental conditions, treatments and exposures ontology (ECTO)¶
Download the lastest version of the database we created:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/ecto202302.db.gz
gunzip -N ecto202302.db.gz
Now to calculate the similarity between water vapour saturated air and pressure of air execute:
ssmpy.semantic_base("ecto.db")
e1 = ssmpy.get_id("ENVO:01000829")
e2 = ssmpy.get_id("ENVO:09200011")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
0.4783673341124399
0.052996013929053794
0.05526015750810917
To create an updated version of the database, download the ontology:
curl -L -O http://purl.obolibrary.org/obo/ecto.owl
And then create the new database:
ssmpy.create_semantic_base("ecto.owl", "ecto.db", "http://purl.obolibrary.org/obo/", "http://www.w3.org/2000/01/rdf-schema#subClassOf", '')
Environment Ontology (ENVO)¶
Download the lastest version of the database we created:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/envo202302.db.gz
gunzip -N envo202302.db.gz
Now to calculate the similarity between water vapour saturated air and pressure of air execute:
ssmpy.semantic_base("envo.db")
e1 = ssmpy.get_id("ENVO:01000829")
e2 = ssmpy.get_id("ENVO:09200011")
ssmpy.ssm_resnik(e1,e2)
ssmpy.ssm_lin(e1,e2)
ssmpy.ssm_jiang_conrath(e1,e2)
Output:
0.10095128489526445
0.012723633698085534
0.06000093158424831
To create an updated version of the database, download the ontology:
curl -L -O http://purl.obolibrary.org/obo/envo.owl
And then create the new database:
ssmpy.create_semantic_base("envo.owl", "envo.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:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/mesh202302.db.gz
gunzip -N mesh202302.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:
3.652230798230297
0.510133103212917
0.1247773485102854
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:
curl -L -O 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:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/radlex202302.db.gz
gunzip -N radlex202302.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:
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/wordnet202302.db.gz
gunzip -N wordnet202302.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:
curl -L -O 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", '')