[](https://pepy.tech/project/ssmpy)
# DiShIn: Semantic Similarity Measures using Disjunctive Shared Information
This software package offers essential functions for utilizing semantic similarity measures directly from RDF or OWL files.
Access the web tool utilizing this package here: [DiShIn Demo](https://labs.rd.ciencias.ulisboa.pt/dishin/)
Package documentation: https://dishin.readthedocs.io/en/latest/
## New Stuff
### 2024
- **DATABASES**: Databases updated: 202407
### 2023
- **ONTOLOGIES**: New examples added, namely the ontologies: OSCI, CL, ENVO, and ECTO.
### 2021
- **DOCKER**: Image available: [fjmc/dishin-image](https://hub.docker.com/r/fjmc/dishin-image)
## References:
- **Semantic similarity definition**
F. Couto and A. Lamurias
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
[DOI: 10.1016/B978-0-12-809633-8.20401-9](https://doi.org/10.1016/B978-0-12-809633-8.20401-9)
[ResearchGate](https://www.researchgate.net/publication/323219905_Semantic_Similarity_Definition)
## System Requirements
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/go202407.db.gz
gunzip -N go202407.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 5.425897125338367
Resnik MICA intrinsic 8.817112581532497
Lin DiShIn intrinsic 0.592108571115022
Lin MICA intrinsic 0.9621796748838876
JC DiShIn intrinsic 0.11798605228261819
JC MICA intrinsic 0.5906161091496418
Resnik DiShIn extrinsic 5.996307081288803
Resnik MICA extrinsic 10.06498546096925
Lin DiShIn extrinsic 0.5516531867976029
Lin MICA extrinsic 0.9259668041253548
JC DiShIn extrinsic 0.09305100083697551
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 1.0460749575571253
Resnik MICA intrinsic 1.0460749575571253
Lin DiShIn intrinsic 0.12881766476333187
Lin MICA intrinsic 0.12881766476333187
JC DiShIn intrinsic 0.07376784927866546
JC MICA intrinsic 0.07376784927866546
Resnik DiShIn extrinsic 0.4593770683280119
Resnik MICA extrinsic 0.4593770683280119
Lin DiShIn extrinsic 0.09032548949936249
Lin MICA extrinsic 0.09032548949936249
JC DiShIn extrinsic 0.09082772814721791
JC MICA extrinsic 0.09082772814721791
```
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 https://release.geneontology.org/2024-06-17/annotations/filtered_goa_uniprot_all_noiea.gaf.gz
gunzip filtered_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 filtered_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/chebi202407.db.gz
gunzip -N chebi202407.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 1.5055300158880238
Resnik MICA intrinsic 5.806757844273615
Lin DiShIn intrinsic 0.13081472985683726
Lin MICA intrinsic 0.5045462068019113
JC DiShIn intrinsic 0.04760389486434374
JC MICA intrinsic 0.08061766783674874
```
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/hp202407.db.gz
gunzip -N hp202407.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 4.670874222643114
Resnik MICA intrinsic 6.024899323194219
Lin DiShIn intrinsic 0.5111252236334636
Lin MICA intrinsic 0.6592937140135965
JC DiShIn intrinsic 0.10065343384373353
JC MICA intrinsic 0.13836941515802242
```
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/doid202407.db.gz
gunzip -N doid202407.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 2.432559719491104
Resnik MICA intrinsic 3.894842361456266
Lin DiShIn intrinsic 0.4393904394647951
Lin MICA intrinsic 0.7035208562955191
JC DiShIn intrinsic 0.13874802978811976
JC MICA intrinsic 0.23349514659345028
```
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/osci202407.db.gz
gunzip -N osci202407.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/cl202407.db.gz
gunzip -N cl202407.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 1.7731416175429349
Resnik MICA intrinsic 2.504915112519477
Lin DiShIn intrinsic 0.3003331215124555
Lin MICA intrinsic 0.4242802534346819
JC DiShIn intrinsic 0.10797329434564479
JC MICA intrinsic 0.12823797003111145
```
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/ecto202407.db.gz
gunzip -N ecto202407.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 0.3741060286214311
Resnik MICA intrinsic 0.3741060286214311
Lin DiShIn intrinsic 0.0435162446031204
Lin MICA intrinsic 0.0435162446031204
JC DiShIn intrinsic 0.057320898271413026
JC MICA intrinsic 0.057320898271413026
```
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/envo202407.db.gz
gunzip -N envo202407.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 0.09259857636092807
Resnik MICA intrinsic 0.09259857636092807
Lin DiShIn intrinsic 0.011463181071745975
Lin MICA intrinsic 0.011463181071745975
JC DiShIn intrinsic 0.05892533743160689
JC MICA intrinsic 0.05892533743160689
```
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/mesh202407.db.gz
gunzip -N mesh202407.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 3.6682439022751536
Resnik MICA intrinsic 6.131468006209348
Lin DiShIn intrinsic 0.5110825484632721
Lin MICA intrinsic 0.8542742461838173
JC DiShIn intrinsic 0.12471452425193749
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 ''
```
### WordNet Example
Download the lastest version of the database we created:
```shell
curl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/wordnet202407.db.gz
gunzip -N wordnet202407.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": null,
"maintainer_email": "fjcouto@ciencias.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/30/54/b3bc2f4d7623fb181b676afa8c72c991c58a271fb8b70ae1dabaf51f5d6e/ssmpy-0.2.7.tar.gz",
"platform": null,
"description": "[](https://pepy.tech/project/ssmpy)\n\n# DiShIn: Semantic Similarity Measures using Disjunctive Shared Information\n\nThis software package offers essential functions for utilizing semantic similarity measures directly from RDF or OWL files.\n\nAccess the web tool utilizing this package here: [DiShIn Demo](https://labs.rd.ciencias.ulisboa.pt/dishin/)\n\nPackage documentation: https://dishin.readthedocs.io/en/latest/\n\n## New Stuff\n\n### 2024\n- **DATABASES**: Databases updated: 202407\n\n### 2023\n- **ONTOLOGIES**: New examples added, namely the ontologies: OSCI, CL, ENVO, and ECTO.\n\n### 2021\n- **DOCKER**: Image available: [fjmc/dishin-image](https://hub.docker.com/r/fjmc/dishin-image)\n\n## References: \n- **Semantic similarity definition** \n F. Couto and A. Lamurias \n 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 [DOI: 10.1016/B978-0-12-809633-8.20401-9](https://doi.org/10.1016/B978-0-12-809633-8.20401-9) \n [ResearchGate](https://www.researchgate.net/publication/323219905_Semantic_Similarity_Definition)\n\n## System Requirements\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/go202407.db.gz\ngunzip -N go202407.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 5.425897125338367\nResnik MICA intrinsic 8.817112581532497\nLin DiShIn intrinsic 0.592108571115022\nLin MICA intrinsic 0.9621796748838876\nJC DiShIn intrinsic 0.11798605228261819\nJC MICA intrinsic 0.5906161091496418\nResnik DiShIn extrinsic 5.996307081288803\nResnik MICA extrinsic 10.06498546096925\nLin DiShIn extrinsic 0.5516531867976029\nLin MICA extrinsic 0.9259668041253548\nJC DiShIn extrinsic 0.09305100083697551\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 1.0460749575571253\nResnik MICA intrinsic 1.0460749575571253\nLin DiShIn intrinsic 0.12881766476333187\nLin MICA intrinsic 0.12881766476333187\nJC DiShIn intrinsic 0.07376784927866546\nJC MICA intrinsic 0.07376784927866546\nResnik DiShIn extrinsic 0.4593770683280119\nResnik MICA extrinsic 0.4593770683280119\nLin DiShIn extrinsic 0.09032548949936249\nLin MICA extrinsic 0.09032548949936249\nJC DiShIn extrinsic 0.09082772814721791\nJC MICA extrinsic 0.09082772814721791\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 https://release.geneontology.org/2024-06-17/annotations/filtered_goa_uniprot_all_noiea.gaf.gz\ngunzip filtered_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 filtered_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/chebi202407.db.gz\ngunzip -N chebi202407.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 1.5055300158880238\nResnik MICA intrinsic 5.806757844273615\nLin DiShIn intrinsic 0.13081472985683726\nLin MICA intrinsic 0.5045462068019113\nJC DiShIn intrinsic 0.04760389486434374\nJC MICA intrinsic 0.08061766783674874\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/hp202407.db.gz\ngunzip -N hp202407.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 4.670874222643114\nResnik MICA intrinsic 6.024899323194219\nLin DiShIn intrinsic 0.5111252236334636\nLin MICA intrinsic 0.6592937140135965\nJC DiShIn intrinsic 0.10065343384373353\nJC MICA intrinsic 0.13836941515802242\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/doid202407.db.gz\ngunzip -N doid202407.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 2.432559719491104\nResnik MICA intrinsic 3.894842361456266\nLin DiShIn intrinsic 0.4393904394647951\nLin MICA intrinsic 0.7035208562955191\nJC DiShIn intrinsic 0.13874802978811976\nJC MICA intrinsic 0.23349514659345028\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/osci202407.db.gz\ngunzip -N osci202407.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/cl202407.db.gz\ngunzip -N cl202407.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 1.7731416175429349\nResnik MICA intrinsic 2.504915112519477\nLin DiShIn intrinsic 0.3003331215124555\nLin MICA intrinsic 0.4242802534346819\nJC DiShIn intrinsic 0.10797329434564479\nJC MICA intrinsic 0.12823797003111145\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/ecto202407.db.gz\ngunzip -N ecto202407.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 0.3741060286214311\nResnik MICA intrinsic 0.3741060286214311\nLin DiShIn intrinsic 0.0435162446031204\nLin MICA intrinsic 0.0435162446031204\nJC DiShIn intrinsic 0.057320898271413026\nJC MICA intrinsic 0.057320898271413026\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/envo202407.db.gz\ngunzip -N envo202407.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 0.09259857636092807\nResnik MICA intrinsic 0.09259857636092807\nLin DiShIn intrinsic 0.011463181071745975\nLin MICA intrinsic 0.011463181071745975\nJC DiShIn intrinsic 0.05892533743160689\nJC MICA intrinsic 0.05892533743160689\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/mesh202407.db.gz\ngunzip -N mesh202407.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 3.6682439022751536\nResnik MICA intrinsic 6.131468006209348\nLin DiShIn intrinsic 0.5110825484632721\nLin MICA intrinsic 0.8542742461838173\nJC DiShIn intrinsic 0.12471452425193749\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### WordNet Example\n\nDownload the lastest version of the database we created:\n```shell\ncurl -L -O http://labs.rd.ciencias.ulisboa.pt/dishin/wordnet202407.db.gz\ngunzip -N wordnet202407.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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