Metadata-Version: 2.4
Name: assay-extract
Version: 0.1.1
Summary: Extraction of biomedical outcome measures from text
Home-page: https://github.com/Ineichen-Group/AssayExtract
Author: Simona E. Doneva
License: MIT
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# AssayExtract

**AssayExtract** is a Python package for extracting and standardizing biomedical outcome measures (assays) from research text.

It identifies assays mentioned an input text and maps them to a curated vocabulary of canonical names, outcome domains, and synonyms.

---

## Motivation

Reliable extraction of outcome measures from biomedical literature is challenging due to inconsistent reporting and high variability in terminology. Prior work ([PreClinIE, ACL BioNLP 2025](https://aclanthology.org/2025.bionlp-1.8/)) found that manual annotations of outcome measures showed **low inter-annotator agreement**, making them unsuitable for training robust machine learning models.

To address this, AssayExtract adopts a **rule-based approach grounded in a curated assay vocabulary**.

---

## Approach

AssayExtract is built on a **harmonized vocabulary of outcome assessment techniques**, developed through manual curation of the biomedical literature.

- A core set of commonly used assays was identified from representative studies  
- Each assay was assigned a **canonical name** and mapped to one of five outcome domains  
- Synonyms and lexical variants were expanded using a large language model and manually reviewed  
- A domain-specific **synonym dictionary** enables robust matching via pattern-based extraction  

Extracted mentions are normalized to canonical names and linked to structured metadata, including domain and subdomain.

---

## Outcome Domains


| Outcome Domain | N | Canonical Name Examples | References |
|---|---:|---|---|
| Molecular & Cellular | 204 | acetylomics; apoptosis -- caspase-3; molsoft icm-pro | [Adil et al., 2021](#adil-et-al-2021); [Chen et al., 2023](#chen-et-al-2023); [Dufva, 2009](#dufva-2009); [Guevara et al., 2022](#guevara-et-al-2022); [Jin & Kennedy, 2015](#jin--kennedy-2015); [Just, 2021](#just-2021); [Musumeci, 2014](#musumeci-2014); [Osier et al., 2015](#osier-et-al-2015); [Pai & Satpathy, 2021](#pai--satpathy-2021); [Verma et al., 2025](#verma-et-al-2025) |
| Behavioral | 192 | delayed matching-to-place water maze; geller-seifter conflict test; radial arm water maze | [Acikgoz et al., 2022](#acikgoz-et-al-2022); [Burrows et al., 2019](#burrows-et-al-2019); [Choi & Kumar, 2024](#choi--kumar-2024); [Gold et al., 2013](#gold-et-al-2013); [Gregory et al., 2013](#gregory-et-al-2013); [Guevara et al., 2022](#guevara-et-al-2022); [Harrison et al., 2020](#harrison-et-al-2020); [Jones et al., 2025](#jones-et-al-2025); [Meredith & Kang, 2006](#meredith--kang-2006); [Osier et al., 2015](#osier-et-al-2015); [Pinkernell et al., 2016](#pinkernell-et-al-2016); [Sadler et al., 2022](#sadler-et-al-2022); [Shepherd et al., 2016](#shepherd-et-al-2016); [Wahl et al., 2017](#wahl-et-al-2017); [Webster et al., 2014](#webster-et-al-2014); [Xiong et al., 2013](#xiong-et-al-2013); [Zarruk et al., 2011](#zarruk-et-al-2011) |
| Imaging | 97 | head-mounted three-photon miniscope; optical intrinsic signal; zte-cbv fmri | [Jones et al., 2025](#jones-et-al-2025); [Just, 2021](#just-2021); [Markicevic et al., 2021](#markicevic-et-al-2021); [Osier et al., 2015](#osier-et-al-2015); [Tremoleda & Sosabowski, 2015](#tremoleda--sosabowski-2015); [Waerzeggers et al., 2010](#waerzeggers-et-al-2010) |
| Histology | 74 | frozen section; polarizing microscopy; sirius red | [Alturkistani et al., 2016](#alturkistani-et-al-2016); [Gurina & Simms, 2025](#gurina--simms-2025); [Javaeed et al., 2021](#javaeed-et-al-2021); [Jones et al., 2025](#jones-et-al-2025); [Mark et al., 2007](#mark-et-al-2007); [Markicevic et al., 2021](#markicevic-et-al-2021); [Musumeci, 2014](#musumeci-2014); [Osier et al., 2015](#osier-et-al-2015) |
| Physiology | 62 | adrenal weight; single-unit extracellular recording; thymus weight | [Alemán et al., 2000](#alemán-et-al-2000); [Burrows et al., 2019](#burrows-et-al-2019); [Guevara et al., 2022](#guevara-et-al-2022); [Markicevic et al., 2021](#markicevic-et-al-2021); [Osier et al., 2015](#osier-et-al-2015); [Pinkernell et al., 2016](#pinkernell-et-al-2016); [Wickenden, 2000](#wickenden-2000) |
---

## Use Cases

- Literature mining and systematic reviews  
- Analysis of outcome measures across studies  
- Construction of structured datasets for biomedical NLP and LLMs  

---

## Installation

```bash
pip install assay-extract
```

Or from source:

```bash
git clone https://github.com/Ineichen-Group/AssayExtract.git
cd AssayExtract
pip install -e .
```

## Quick Start

```python
from assay_extract import AssayClassifier

classifier = AssayClassifier()

methods = """
We assessed anxiety using the elevated plus maze and social behavior 
with the three-chamber test. Learning was measured on the morris water maze.
Motor coordination was tested on the accelerating rotarod.
"""

results = classifier.extract_measures(methods)

for result in results:
    print(f"{result.canonical_name}")
    print(f"  Domain: {result.outcome_domain}")
    print(f"  Subdomain: {result.subdomain}")
    print()
```

Output:
```
elevated plus maze
  Domain: Behavioral
  Subdomain: Anxiety

three-chamber social approach test
  Domain: Behavioral
  Subdomain: Sociability

morris water maze
  Domain: Behavioral
  Subdomain: Cognition & learning

accelerating rotarod
  Domain: Behavioral
  Subdomain: Motor coordination
```

## Testing

```bash
python -m pytest assay_extract/tests/ -v
```

All 9 tests pass.

## License

MIT License

## Citation

```bibtex
@software{assayextract2025,
  title={AssayExtract: Extraction of Biomedical Outcome Measures from Text},
  author={Simona Emilova Doneva},
  year={2025},
  url={https://github.com/Ineichen-Group/AssayExtract}
}
```

## References

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### Adil et al. 2021
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### Alturkistani et al. 2016
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