| Name | preon JSON |
| Version |
0.1.2
JSON |
| download |
| home_page | https://github.com/ermshaua/preon |
| Summary | None |
| upload_time | 2025-07-24 12:50:27 |
| maintainer | None |
| docs_url | None |
| author | Arik Ermshaus |
| requires_python | <3.13,>=3.7 |
| license | BSD 3-Clause License
Copyright (c) 2021, Arik Ermshaus
All rights reserved.
Redistribution and use in source and binary forms, with or without
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1. Redistributions of source code must retain the above copyright notice, this
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2. Redistributions in binary form must reproduce the above copyright notice,
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3. Neither the name of the copyright holder nor the names of its
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
| keywords |
precision-oncology
data-integration
text-mining
normalization
drug-names
cancer-types
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
daproli
jellyfish
nltk
numpy
pandas
pronto
tqdm
lxml
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# preon (PREcision Oncology Normalization)
preon is a fuzzy search tool for medical entities.
## Installation
You can install preon with PyPi:
`python -m pip install preon`
## Examples
Let's first import the normalizer and EBI drug names with CHEMBL ids.
```python3
>>> from preon.normalization import PrecisionOncologyNormalizer
>>> from preon.drug import store_ebi_drugs, load_ebi_drugs
```
Please download the <a href="https://www.ebi.ac.uk/chembl/explore/compounds/">EBI compound CSV file</a> and store it as a local resource. This step only has to be performed when the resource file is created or updated.
```python3
>>> store_ebi_drugs("/Users/Username/Downloads/compounds.csv")
```
Next, we can fit the normalizer with the drug names and ids as its reference data.
```python3
>>> drug_names, chembl_ids = load_ebi_drugs()
>>> normalizer = PrecisionOncologyNormalizer().fit(drug_names, chembl_ids)
```
We can now search for drug names and retrieve their CHEMBL ids. Let's search for the cancer drug "Avastin".
```python3
>>> normalizer.query("Avastin")
(['avastin'], [['CHEMBL1201583']], {'match_type': 'exact'})
```
As a result for our query, we get list of matching normalized drug names (in this case `['avastin']`), a list of associated CHEMBL ids for every returned drug name `[['CHEMBL1201583']]` and some meta information about the matching `{'match_type': 'exact'}`. We can also search for multi-token drug names like "Ixabepilone Epothilone B analog" and find CHEMBL ids for the relevant tokens.
```python3
>>> normalizer.query("Ixabepilone Epothilone B analog")
(['ixabepilone'], [['CHEMBL1201752']], {'match_type': 'substring'})
```
We find the relevant drug name `['ixabepilone']` and preon provides the meta information that the matching is based on a substring. On default, preon only looks for 1 matching token. It can also look for n-grams by setting the `n_grams` parameter in the query method. Let's take a harder example, say "Isavuconazonium", but misspell it as "Isavuconaconium".
```python3
>>> normalizer.query("Isavuconaconium")
(['isavuconazonium'], [['CHEMBL1183349']], {'match_type': 'partial', 'edit_distance': 0.067})
```
preon finds the correct drug "Isavuconazonium" and provides the meta information that it is a partial match with 7% distance. It returns drug names with a distance smaller than 20% on default. In order to change this parameter, set the `threshold` argument in the query method. If preon cannot normalize the query, it returns `None` and issues a user warning.
```python3
>>> normalizer.query("risolipase en.")
preon/normalization.py:50: UserWarning: Cannot match risolipase en. to reference data. Try changing the partial matching threshold or number of n-grams.
```
For automatic data integrations, warnings can be stored in a logging file, see e.g. <a href="https://github.com/ermshaua/preon/blob/main/preon/examples/drug_name_normalization.ipynb">here</a>. In a similar fashion, you can also normalize cancer types or genes. We provide gold standards for preon with which we test it. For more detail, see the example <a href="https://github.com/ermshaua/preon/tree/main/preon/examples">notebooks</a>. We also use preon in practice to normalize and integrate medical data in the PREDICT project.
## Citation
The preon package is actively maintained, updated and intended for application. If you use it in your scientific publication, we would appreciate the following <a href="https://doi.org/10.1093/bioinformatics/btae085" target="_blank">citation</a>:
```
@article{preon2023,
title={preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology},
author={Arik Ermshaus and Michael Piechotta and Gina R{\"u}ter and Ulrich Keilholz and Ulf Leser and Manuela Benary},
journal={Bioinformatics},
year={2023},
volume={40}
}
```
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"description": "# preon (PREcision Oncology Normalization)\npreon is a fuzzy search tool for medical entities.\n\n## Installation\n\nYou can install preon with PyPi:\n`python -m pip install preon`\n\n## Examples\n\nLet's first import the normalizer and EBI drug names with CHEMBL ids.\n\n```python3\n>>> from preon.normalization import PrecisionOncologyNormalizer\n>>> from preon.drug import store_ebi_drugs, load_ebi_drugs\n```\n\nPlease download the <a href=\"https://www.ebi.ac.uk/chembl/explore/compounds/\">EBI compound CSV file</a> and store it as a local resource. This step only has to be performed when the resource file is created or updated. \n\n```python3\n>>> store_ebi_drugs(\"/Users/Username/Downloads/compounds.csv\")\n```\n\nNext, we can fit the normalizer with the drug names and ids as its reference data.\n\n```python3\n>>> drug_names, chembl_ids = load_ebi_drugs()\n>>> normalizer = PrecisionOncologyNormalizer().fit(drug_names, chembl_ids)\n```\n\nWe can now search for drug names and retrieve their CHEMBL ids. Let's search for the cancer drug \"Avastin\".\n\n```python3\n>>> normalizer.query(\"Avastin\")\n(['avastin'], [['CHEMBL1201583']], {'match_type': 'exact'})\n```\n\nAs a result for our query, we get list of matching normalized drug names (in this case `['avastin']`), a list of associated CHEMBL ids for every returned drug name `[['CHEMBL1201583']]` and some meta information about the matching `{'match_type': 'exact'}`. We can also search for multi-token drug names like \"Ixabepilone Epothilone B analog\" and find CHEMBL ids for the relevant tokens.\n\n```python3\n>>> normalizer.query(\"Ixabepilone Epothilone B analog\")\n(['ixabepilone'], [['CHEMBL1201752']], {'match_type': 'substring'})\n```\n\nWe find the relevant drug name `['ixabepilone']` and preon provides the meta information that the matching is based on a substring. On default, preon only looks for 1 matching token. It can also look for n-grams by setting the `n_grams` parameter in the query method. Let's take a harder example, say \"Isavuconazonium\", but misspell it as \"Isavuconaconium\".\n\n```python3\n>>> normalizer.query(\"Isavuconaconium\")\n(['isavuconazonium'], [['CHEMBL1183349']], {'match_type': 'partial', 'edit_distance': 0.067})\n```\n\npreon finds the correct drug \"Isavuconazonium\" and provides the meta information that it is a partial match with 7% distance. It returns drug names with a distance smaller than 20% on default. In order to change this parameter, set the `threshold` argument in the query method. If preon cannot normalize the query, it returns `None` and issues a user warning.\n\n```python3\n>>> normalizer.query(\"risolipase en.\")\npreon/normalization.py:50: UserWarning: Cannot match risolipase en. to reference data. Try changing the partial matching threshold or number of n-grams.\n```\n\nFor automatic data integrations, warnings can be stored in a logging file, see e.g. <a href=\"https://github.com/ermshaua/preon/blob/main/preon/examples/drug_name_normalization.ipynb\">here</a>. In a similar fashion, you can also normalize cancer types or genes. We provide gold standards for preon with which we test it. For more detail, see the example <a href=\"https://github.com/ermshaua/preon/tree/main/preon/examples\">notebooks</a>. We also use preon in practice to normalize and integrate medical data in the PREDICT project.\n\n## Citation\n\nThe preon package is actively maintained, updated and intended for application. If you use it in your scientific publication, we would appreciate the following <a href=\"https://doi.org/10.1093/bioinformatics/btae085\" target=\"_blank\">citation</a>:\n\n```\n@article{preon2023,\n title={preon: Fast and accurate entity normalization for drug names and cancer types in precision oncology},\n author={Arik Ermshaus and Michael Piechotta and Gina R{\\\"u}ter and Ulrich Keilholz and Ulf Leser and Manuela Benary},\n journal={Bioinformatics},\n year={2023},\n volume={40}\n}\n```\n",
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