# DiShIn: Semantic Similarity Measures using Disjunctive Shared Information
[](https://pepy.tech/project/ssmpy)
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/
Package documentation: https://dishin.readthedocs.io/en/latest/
** **NEW** **
- New examples addes, namely the ontologies: OSCI, CL, ECTO, and ENVO
- Databases updated: 202302
- Docker image available: https://hub.docker.com/r/fjmc/dishin-image
## 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]
### INSTALLATION
Either clone this repository or install from pypi:
```
pip install ssmpy
```
If you use it from the shell, you need to install python3, sqlite3, rdflib and pandas:
```shell
sudo apt-get update
sudo apt-get install python3 python3-rdflib python3-pandas sqlite3
```
and then clone and enter the folder:
```shell
git clone https://github.com/lasigeBioTM/DiShIn.git
cd DiShIn
```
If you just have python2 or you cannot install packages,
then create and use a lighter version of DiShIn:
```shell
curl https://raw.githubusercontent.com/lasigeBioTM/DiShIn/master/dishin.py | sed -e 's/import ssmpy/import ssm\nimport annotations/; s/ssmpy\.ssm\./ssm./g; s/ssmpy\./ssm./g; s/ssm.get_uniprot_annotations/annotations.get_uniprot_annotations/g' > dishin.py
curl https://raw.githubusercontent.com/lasigeBioTM/DiShIn/master/ssmpy/ssm.py | sed 's/from ssmpy./# from ssmpy./' > ssm.py
curl https://raw.githubusercontent.com/lasigeBioTM/DiShIn/master/ssmpy/annotations.py | sed 's/import ssmpy./import /; s/ssmpy./ssm./' > annotations.py
```
Note, this light version cannot create new databases.
## USAGE:
You can use DiShIn as a command line tool with the dishin.py script of this repository:
```shell
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:
```python
>>> import ssmpy
```
You can find more usage examples at https://dishin.readthedocs.io/en/latest/other_examples.html.
### Metals Example
To create the semantic base file (_metals.db_) from the _metals.owl_ file:
```shell
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.
```txt
gold
silver
gold
silver
copper
platinum
palladium
metal
coinage
precious
```
Now to calculate the similarity between _copper_ and _gold_ execute:
```shell
python dishin.py metals.db copper gold
```
Output:
```txt
Resnik DiShIn intrinsic 0.2938933324510595
Resnik MICA intrinsic 0.587786664902119
Lin DiShIn intrinsic 0.19539774554219633
Lin MICA intrinsic 0.39079549108439265
JC DiShIn intrinsic 0.29236619053475066
JC MICA intrinsic 0.35303485982596094
Resnik DiShIn extrinsic 0.22599256187152864
Resnik MICA extrinsic 0.45198512374305727
Lin DiShIn extrinsic 0.1504595366201814
Lin MICA extrinsic 0.3009190732403628
JC DiShIn extrinsic 0.281527889373394
JC MICA extrinsic 0.322574315537045
```
Using the python function directly (first download metals.db and metals.txt from this repository):
```python
>>> 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 latest version of the database we created:
```shell
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_ execute:
```shell
python dishin.py go.db GO_0000023 GO_0000025
```
Output:
```txt
Resnik DiShIn intrinsic 3.78921500847647
Resnik MICA intrinsic 8.851627638869019
Lin DiShIn intrinsic 0.41195174078911123
Lin MICA intrinsic 0.9623215907495385
JC DiShIn intrinsic 0.08461688373053834
JC MICA intrinsic 0.5906161091496406
Resnik DiShIn extrinsic 4.310175268311233
Resnik MICA extrinsic 10.13641508423437
Lin DiShIn extrinsic 0.39394227804683757
Lin MICA extrinsic 0.9264501327520663
JC DiShIn extrinsic 0.07011679859284012
JC MICA extrinsic 0.3832242933372551
```
Now to calculate the similarity between proteins [Q12345](http://www.uniprot.org/uniprot/Q12345) and [Q12346](http://www.uniprot.org/uniprot/Q12346) execute:
```shell
python dishin.py go.db Q12345 Q12346
```
Output:
```txt
Resnik DiShIn intrinsic 1.0570711021304315
Resnik MICA intrinsic 1.0570711021304315
Lin DiShIn intrinsic 0.12958973249230304
Lin MICA intrinsic 0.12958973249230304
JC DiShIn intrinsic 0.07363762393108407
JC MICA intrinsic 0.07363762393108407
Resnik DiShIn extrinsic 0.47838902966531843
Resnik MICA extrinsic 0.47838902966531843
Lin DiShIn extrinsic 0.09255485176480098
Lin MICA extrinsic 0.09255485176480098
JC DiShIn extrinsic 0.09036345699860157
JC MICA extrinsic 0.09036345699860157
```
To create an updated version of the database, download the ontology and annotations:
```shell
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
```
And then create the new database:
```shell
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
```
### Chemical Entities of Biological Interest (ChEBI) Example
Download the lastest version of the database we created:
```shell
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:
```shell
python dishin.py chebi.db CHEBI_31236 CHEBI_3131
```
Output:
```txt
Resnik DiShIn intrinsic 1.476632415671022
Resnik MICA intrinsic 5.641314851207291
Lin DiShIn intrinsic 0.13064609500712546
Lin MICA intrinsic 0.4991193124261839
JC DiShIn intrinsic 0.048421904489541145
JC MICA intrinsic 0.08115272103316418
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O http://purl.obolibrary.org/obo/chebi/chebi_lite.owl
```
And then create the new database:
```shell
python dishin.py 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:
```shell
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:
```shell
python dishin.py hp.db HP_0000588 HP_0001093
```
Output:
```txt
Resnik DiShIn intrinsic 4.617876786673204
Resnik MICA intrinsic 5.995918897457362
Lin DiShIn intrinsic 0.5108021406767812
Lin MICA intrinsic 0.6632329855539579
JC DiShIn intrinsic 0.10157307132754399
JC MICA intrinsic 0.14106272672409434
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O http://purl.obolibrary.org/obo/hp.owl
```
And then create the new database:
```shell
python dishin.py 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:
```shell
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:
```shell
python dishin.py doid.db DOID_2841 DOID_1324
```
Output:
```txt
Resnik DiShIn intrinsic 2.4006601711893345
Resnik MICA intrinsic 3.8437441646748516
Lin DiShIn intrinsic 0.4363310773119593
Lin MICA intrinsic 0.6986182602651521
JC DiShIn intrinsic 0.13884016970564805
JC MICA intrinsic 0.23167677096337774
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O http://purl.obolibrary.org/obo/doid.owl
```
And then create the new database:
```shell
python dishin.py doid.owl doid.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''
```
### Ontology for Stem Cell Investigations (OSCI) Example
Download the lastest version of the database we created:
```shell
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:
```shell
python dishin.py osci.db CL_0000047 CL_0000101
```
Output:
```txt
Resnik DiShIn intrinsic 3.1560550137337486
Resnik MICA intrinsic 4.1255971057262055
Lin DiShIn intrinsic 0.6369806275261506
Lin MICA intrinsic 0.8326614782980606
JC DiShIn intrinsic 0.21751839842873807
JC MICA intrinsic 0.3761904438530041
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O https://raw.githubusercontent.com/stemcellontologyresource/OSCI/master/src/ontology/osci.owl
```
And then create the new database:
```shell
python dishin.py osci.owl osci.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''
```
### Cell Ontology (CL) Example
Download the lastest version of the database we created:
```shell
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:
```shell
python dishin.py cl.db CL_0000047 CL_0000101
```
Output:
```txt
Resnik DiShIn intrinsic 1.9063375764548978
Resnik MICA intrinsic 2.6866433997198857
Lin DiShIn intrinsic 0.3272258822885516
Lin MICA intrinsic 0.4611666200814948
JC DiShIn intrinsic 0.11313710298029131
JC MICA intrinsic 0.1373961991624287
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O http://purl.obolibrary.org/obo/cl.owl
```
And then create the new database:
```shell
python dishin.py 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) Example
Download the lastest version of the database we created:
```shell
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:
```shell
python dishin.py ecto.db ENVO_01000829 ENVO_09200011
```
Output:
```txt
Resnik DiShIn intrinsic 0.4783673341124399
Resnik MICA intrinsic 0.4783673341124399
Lin DiShIn intrinsic 0.052996013929053794
Lin MICA intrinsic 0.052996013929053794
JC DiShIn intrinsic 0.05526015750810917
JC MICA intrinsic 0.05526015750810917
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O http://purl.obolibrary.org/obo/ecto.owl
```
And then create the new database:
```shell
python dishin.py ecto.owl ecto.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''
```
### Environment Ontology (ENVO) Example
Download the lastest version of the database we created:
```shell
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:
```shell
python dishin.py envo.db ENVO_01000829 ENVO_09200011
```
Output:
```txt
Resnik DiShIn intrinsic 0.10095128489526445
Resnik MICA intrinsic 0.10095128489526445
Lin DiShIn intrinsic 0.012723633698085534
Lin MICA intrinsic 0.012723633698085534
JC DiShIn intrinsic 0.06000093158424831
JC MICA intrinsic 0.06000093158424831
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O http://purl.obolibrary.org/obo/envo.owl
```
And then create the new database:
```shell
python dishin.py 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:
```shell
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:
```shell
python dishin.py mesh.db D008305 D005334
```
Output:
```txt
Resnik DiShIn intrinsic 3.652230798230297
Resnik MICA intrinsic 6.113436328571005
Lin DiShIn intrinsic 0.510133103212917
Lin MICA intrinsic 0.8539072194176971
JC DiShIn intrinsic 0.1247773485102854
JC MICA intrinsic 0.3234294846252581
```
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:
```shell
curl -L -O ftp://nlmpubs.nlm.nih.gov/online/mesh/rdf/mesh.nt.gz
gunzip mesh.nt.gz
```
And then create the new database:
```shell
python dishin.py 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:
```shell
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:
```shell
python dishin.py radlex.db RID16139 RID16140
```
Output:
```txt
Resnik MICA intrinsic 9.366531825151093
Lin MICA intrinsic 0.9310964912333252
JC MICA intrinsic 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:
```shell
python dishin.py radlex.rdf radlex.db http://radlex.org/RID/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''
```
### WordNet Example
Download the lastest version of the database we created:
```shell
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:
```shell
python dishin.py wordnet.db ambulance-noun-1 motorcycle-noun-1
```
Output:
```txt
Resnik MICA intrinsic 6.331085809208157
Lin MICA intrinsic 0.6792379292396559
JC MICA intrinsic 0.14327549414725688
```
To create an updated version of the database, download the ontology:
```shell
curl -L -O http://www.w3.org/2006/03/wn/wn20/rdf/wordnet-hyponym.rdf
```
And then create the new database:
```shell
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 ''
```
## Source Code
- ssmpy/semanticbase.py : provides a function to produce the semantic-base as a SQLite database
- ssmpy/ssm.py : provides the functions to calculate semantic similarity based on the SQLite database
- ssmpy/annotations.py : provides the functions to get the annotations for the given proteins
- dishin.py : executes the functions according to the input given
Raw data
{
"_id": null,
"home_page": "https://github.com/lasigeBioTM/DiShIn",
"name": "ssmpy",
"maintainer": "Francisco M Couto",
"docs_url": null,
"requires_python": "",
"maintainer_email": "fjcouto@edu.ulisboa.pt",
"keywords": "graphs,semantic similarity,ontologies",
"author": "Andre Lamurias",
"author_email": "alamurias@lasige.di.fc.ul.pt",
"download_url": "https://files.pythonhosted.org/packages/04/72/f7e4dd5f5c01f6b81f871159d65687927fe9bebb9c59a1176ec0a217e27d/ssmpy-0.2.6.tar.gz",
"platform": null,
"description": "# DiShIn: Semantic Similarity Measures using Disjunctive Shared Information\n\n[](https://pepy.tech/project/ssmpy)\n\nThis software package provides the basic functions to start using semantic similarity measures directly from a rdf or owl file. \n\nA web tool using this package is available at: http://labs.fc.ul.pt/dishin/\n\nPackage documentation: https://dishin.readthedocs.io/en/latest/\n\n** **NEW** **\n- New examples addes, namely the ontologies: OSCI, CL, ECTO, and ENVO\n- Databases updated: 202302\n- Docker image available: https://hub.docker.com/r/fjmc/dishin-image\n\n## Reference: \n\n- F. Couto and A. Lamurias, \u201cSemantic similarity definition,\u201d in Encyclopedia of Bioinformatics and Computational Biology (S. Ranganathan, K. Nakai, C. Sch\u00f6nbach, and M. Gribskov, eds.), vol. 1, pp. 870\u2013876, Oxford: Elsevier, 2019\n[https://doi.org/10.1016/B978-0-12-809633-8.20401-9]\n[https://www.researchgate.net/publication/323219905_Semantic_Similarity_Definition]\n\n### INSTALLATION\n\nEither clone this repository or install from pypi:\n\n```\npip install ssmpy\n```\n\nIf you use it from the shell, you need to install python3, sqlite3, rdflib and pandas:\n```shell\nsudo apt-get update\nsudo apt-get install python3 python3-rdflib python3-pandas sqlite3\n```\n\nand then clone and enter the folder:\n```shell\ngit clone https://github.com/lasigeBioTM/DiShIn.git\ncd DiShIn\n```\n\nIf you just have python2 or you cannot install packages,\nthen create and use a lighter version of DiShIn: \n```shell\ncurl https://raw.githubusercontent.com/lasigeBioTM/DiShIn/master/dishin.py | sed -e 's/import ssmpy/import ssm\\nimport annotations/; s/ssmpy\\.ssm\\./ssm./g; s/ssmpy\\./ssm./g; s/ssm.get_uniprot_annotations/annotations.get_uniprot_annotations/g' > dishin.py\ncurl https://raw.githubusercontent.com/lasigeBioTM/DiShIn/master/ssmpy/ssm.py | sed 's/from ssmpy./# from ssmpy./' > ssm.py\ncurl https://raw.githubusercontent.com/lasigeBioTM/DiShIn/master/ssmpy/annotations.py | sed 's/import ssmpy./import /; s/ssmpy./ssm./' > annotations.py\n```\nNote, this light version cannot create new databases.\n\n## USAGE: \n\nYou can use DiShIn as a command line tool with the dishin.py script of this repository:\n\n```shell\npython dishin.py <semanticbase>.db <term1> <term2>\npython dishin.py <semanticbase>.[owl|rdf] <semanticbase>.db <name_prefix> <relation> <annotation_file>\n```\n\nor use the python functions directly:\n\n```python\n>>> import ssmpy\n```\n\nYou can find more usage examples at https://dishin.readthedocs.io/en/latest/other_examples.html.\n\n\n### Metals Example\n\nTo create the semantic base file (_metals.db_) from the _metals.owl_ file:\n```shell\npython 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\n```\n\nThe _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.\n```txt\ngold\nsilver\ngold\nsilver\ncopper\nplatinum\npalladium\nmetal\ncoinage\nprecious\n```\n\nNow to calculate the similarity between _copper_ and _gold_ execute:\n\n```shell\npython dishin.py metals.db copper gold\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 0.2938933324510595\nResnik MICA intrinsic 0.587786664902119\nLin DiShIn intrinsic 0.19539774554219633\nLin MICA intrinsic 0.39079549108439265\nJC DiShIn intrinsic 0.29236619053475066\nJC MICA intrinsic 0.35303485982596094\nResnik DiShIn extrinsic 0.22599256187152864\nResnik MICA extrinsic 0.45198512374305727\nLin DiShIn extrinsic 0.1504595366201814\nLin MICA extrinsic 0.3009190732403628\nJC DiShIn extrinsic 0.281527889373394\nJC MICA extrinsic 0.322574315537045\n```\n\nUsing the python function directly (first download metals.db and metals.txt from this repository):\n```python\n>>> 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\")\n>>> ssmpy.semantic_base(\"metals.db\")\n>>> e1 = ssmpy.get_id(\"copper\")\n>>> e2 = ssmpy.get_id(\"gold\")\n>>> ssmpy.ssm_resnik (e1,e2)\n```\n\n### Gene Ontology (GO) and UniProt proteins Example\n\nDownload the latest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/go202302.db.gz\ngunzip -N go202302.db.gz\n```\n\nNow to calculate the similarity between _maltose biosynthetic process_ and _maltose catabolic process_ execute:\n```shell\npython dishin.py go.db GO_0000023 GO_0000025\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 3.78921500847647\nResnik MICA intrinsic 8.851627638869019\nLin DiShIn intrinsic 0.41195174078911123\nLin MICA intrinsic 0.9623215907495385\nJC DiShIn intrinsic 0.08461688373053834\nJC MICA intrinsic 0.5906161091496406\nResnik DiShIn extrinsic 4.310175268311233\nResnik MICA extrinsic 10.13641508423437\nLin DiShIn extrinsic 0.39394227804683757\nLin MICA extrinsic 0.9264501327520663\nJC DiShIn extrinsic 0.07011679859284012\nJC MICA extrinsic 0.3832242933372551\n```\n\nNow to calculate the similarity between proteins [Q12345](http://www.uniprot.org/uniprot/Q12345) and [Q12346](http://www.uniprot.org/uniprot/Q12346) execute:\n\n```shell\npython dishin.py go.db Q12345 Q12346\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 1.0570711021304315\nResnik MICA intrinsic 1.0570711021304315\nLin DiShIn intrinsic 0.12958973249230304\nLin MICA intrinsic 0.12958973249230304\nJC DiShIn intrinsic 0.07363762393108407\nJC MICA intrinsic 0.07363762393108407\nResnik DiShIn extrinsic 0.47838902966531843\nResnik MICA extrinsic 0.47838902966531843\nLin DiShIn extrinsic 0.09255485176480098\nLin MICA extrinsic 0.09255485176480098\nJC DiShIn extrinsic 0.09036345699860157\nJC MICA extrinsic 0.09036345699860157\n```\n\nTo create an updated version of the database, download the ontology and annotations:\n```shell\ncurl -L -O http://purl.obolibrary.org/obo/go.owl\ncurl -L -O http://geneontology.org/gene-associations/goa_uniprot_all_noiea.gaf.gz\ngunzip goa_uniprot_all_noiea.gaf.gz \n```\n\nAnd then create the new database:\n```shell\npython 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\n```\n\n### Chemical Entities of Biological Interest (ChEBI) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/chebi202302.db.gz\ngunzip -N chebi202302.db.gz\n```\n\nNow to calculate the similarity between _aripiprazole_ and _bithionol_ execute:\n```shell\npython dishin.py chebi.db CHEBI_31236 CHEBI_3131\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 1.476632415671022\nResnik MICA intrinsic 5.641314851207291\nLin DiShIn intrinsic 0.13064609500712546\nLin MICA intrinsic 0.4991193124261839\nJC DiShIn intrinsic 0.048421904489541145\nJC MICA intrinsic 0.08115272103316418\n```\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O http://purl.obolibrary.org/obo/chebi/chebi_lite.owl\n```\n\nAnd then create the new database:\n```shell\npython dishin.py chebi_lite.owl chebi.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''\n```\n\n### Human Phenotype (HP) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/hp202302.db.gz\ngunzip -N hp202302.db.gz\n```\n\nNow to calculate the similarity between _Optic nerve coloboma_ and _Optic nerve dysplasia_ execute:\n```shell\npython dishin.py hp.db HP_0000588 HP_0001093\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 4.617876786673204\nResnik MICA intrinsic 5.995918897457362\nLin DiShIn intrinsic 0.5108021406767812\nLin MICA intrinsic 0.6632329855539579\nJC DiShIn intrinsic 0.10157307132754399\nJC MICA intrinsic 0.14106272672409434\n```\n\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O http://purl.obolibrary.org/obo/hp.owl\n```\n\nAnd then create the new database:\n```shell\npython dishin.py hp.owl hp.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''\n```\n\n### Human Disease Ontology (HDO) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/doid202302.db.gz\ngunzip -N doid202302.db.gz\n```\n\nNow to calculate the similarity between _Asthma_ and _Lung cancer_ execute:\n```shell\npython dishin.py doid.db DOID_2841 DOID_1324\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 2.4006601711893345\nResnik MICA intrinsic 3.8437441646748516\nLin DiShIn intrinsic 0.4363310773119593\nLin MICA intrinsic 0.6986182602651521\nJC DiShIn intrinsic 0.13884016970564805\nJC MICA intrinsic 0.23167677096337774\n```\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O http://purl.obolibrary.org/obo/doid.owl\n```\n\nAnd then create the new database:\n```shell\npython dishin.py doid.owl doid.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''\n```\n\n### Ontology for Stem Cell Investigations (OSCI) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/osci202302.db.gz\ngunzip -N osci202302.db.gz\n```\n\nNow to calculate the similarity between _neuronal stem cell_ and _sensory neuron_ execute:\n```shell\npython dishin.py osci.db CL_0000047 CL_0000101\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 3.1560550137337486\nResnik MICA intrinsic 4.1255971057262055\nLin DiShIn intrinsic 0.6369806275261506\nLin MICA intrinsic 0.8326614782980606\nJC DiShIn intrinsic 0.21751839842873807\nJC MICA intrinsic 0.3761904438530041\n```\n\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O https://raw.githubusercontent.com/stemcellontologyresource/OSCI/master/src/ontology/osci.owl\n```\n\nAnd then create the new database:\n```shell\npython dishin.py osci.owl osci.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''\n```\n\n\n### Cell Ontology (CL) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/cl202302.db.gz\ngunzip -N cl202302.db.gz\n```\n\nNow to calculate the similarity between _neuronal stem cell_ and _sensory neuron_ execute:\n```shell\npython dishin.py cl.db CL_0000047 CL_0000101\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 1.9063375764548978\nResnik MICA intrinsic 2.6866433997198857\nLin DiShIn intrinsic 0.3272258822885516\nLin MICA intrinsic 0.4611666200814948\nJC DiShIn intrinsic 0.11313710298029131\nJC MICA intrinsic 0.1373961991624287\n```\n\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O http://purl.obolibrary.org/obo/cl.owl\n```\n\nAnd then create the new database:\n```shell\npython dishin.py cl.owl cl.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''\n```\n\n\n### Environmental conditions, treatments and exposures ontology (ECTO) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/ecto202302.db.gz\ngunzip -N ecto202302.db.gz\n```\n\nNow to calculate the similarity between _water vapour saturated air_ and _pressure of air_ execute:\n```shell\npython dishin.py ecto.db ENVO_01000829 ENVO_09200011\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 0.4783673341124399\nResnik MICA intrinsic 0.4783673341124399\nLin DiShIn intrinsic 0.052996013929053794\nLin MICA intrinsic 0.052996013929053794\nJC DiShIn intrinsic 0.05526015750810917\nJC MICA intrinsic 0.05526015750810917\n```\n\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O http://purl.obolibrary.org/obo/ecto.owl\n```\n\nAnd then create the new database:\n```shell\npython dishin.py ecto.owl ecto.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''\n```\n\n### Environment Ontology (ENVO) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/envo202302.db.gz\ngunzip -N envo202302.db.gz\n```\n\nNow to calculate the similarity between _water vapour saturated air_ and _pressure of air_ execute:\n```shell\npython dishin.py envo.db ENVO_01000829 ENVO_09200011\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 0.10095128489526445\nResnik MICA intrinsic 0.10095128489526445\nLin DiShIn intrinsic 0.012723633698085534\nLin MICA intrinsic 0.012723633698085534\nJC DiShIn intrinsic 0.06000093158424831\nJC MICA intrinsic 0.06000093158424831\n```\n\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O http://purl.obolibrary.org/obo/envo.owl\n```\n\nAnd then create the new database:\n```shell\npython dishin.py envo.owl envo.db http://purl.obolibrary.org/obo/ http://www.w3.org/2000/01/rdf-schema#subClassOf ''\n```\n\n### Medical Subject Headings (MeSH) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/mesh202302.db.gz\ngunzip -N mesh202302.db.gz\n```\n\nNow to calculate the similarity between _Malignant Hyperthermia_ and _Fever_ execute:\n```shell\npython dishin.py mesh.db D008305 D005334\n```\n\nOutput:\n```txt\nResnik DiShIn intrinsic 3.652230798230297\nResnik MICA intrinsic 6.113436328571005\nLin DiShIn intrinsic 0.510133103212917\nLin MICA intrinsic 0.8539072194176971\nJC DiShIn intrinsic 0.1247773485102854\nJC MICA intrinsic 0.3234294846252581\n```\n\nTo 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:\n```shell\ncurl -L -O ftp://nlmpubs.nlm.nih.gov/online/mesh/rdf/mesh.nt.gz\ngunzip mesh.nt.gz\n```\nAnd then create the new database:\n```shell\npython dishin.py mesh.nt mesh.db http://id.nlm.nih.gov/mesh/ http://id.nlm.nih.gov/mesh/vocab#broaderDescriptor ''\n```\n\n### Radiology Lexicon (RadLex) Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/radlex202302.db.gz\ngunzip -N radlex202302.db.gz\n```\n\nNow to calculate the similarity between _nervous system of right upper limb_ and _nervous system of left upper limb_ execute:\n```shell\npython dishin.py radlex.db RID16139 RID16140\n```\n\nOutput:\n```txt\nResnik MICA intrinsic 9.366531825151093\nLin MICA intrinsic 0.9310964912333252\nJC MICA intrinsic 0.41905978419640516\n```\n\nTo 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_\n\nAnd then create the new database:\n```shell\npython dishin.py radlex.rdf radlex.db http://radlex.org/RID/ http://www.w3.org/2000/01/rdf-schema#subClassOf '' \n```\n\n### WordNet Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/wordnet202302.db.gz\ngunzip -N wordnet202302.db.gz\n```\n\nNow to calculate the similarity between the nouns _ambulance_ and _motorcycle_ execute:\n```shell\npython dishin.py wordnet.db ambulance-noun-1 motorcycle-noun-1\n```\n\nOutput:\n```txt\nResnik MICA intrinsic 6.331085809208157\nLin MICA intrinsic 0.6792379292396559\nJC MICA intrinsic 0.14327549414725688\n```\n\nTo create an updated version of the database, download the ontology:\n```shell\ncurl -L -O http://www.w3.org/2006/03/wn/wn20/rdf/wordnet-hyponym.rdf\n```\n\nAnd then create the new database:\n```shell\npython 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 ''\n```\n## Source Code \n\n- ssmpy/semanticbase.py : provides a function to produce the semantic-base as a SQLite database \n\n- ssmpy/ssm.py : provides the functions to calculate semantic similarity based on the SQLite database\n\n- ssmpy/annotations.py : provides the functions to get the annotations for the given proteins \n\n- dishin.py : executes the functions according to the input given\n\n\n\n",
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