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.

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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) 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.

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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.

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Outcome Domains

Outcome Domain	N	Canonical Name Examples	References
Molecular & Cellular	204	acetylomics; apoptosis -- caspase-3; molsoft icm-pro	Adil et al., 2021; Chen et al., 2023; Dufva, 2009; Guevara et al., 2022; Jin & Kennedy, 2015; Just, 2021; Musumeci, 2014; Osier et al., 2015; Pai & Satpathy, 2021; Verma et al., 2025
Behavioral	192	delayed matching-to-place water maze; geller-seifter conflict test; radial arm water maze	Acikgoz et al., 2022; Burrows et al., 2019; Choi & Kumar, 2024; Gold et al., 2013; Gregory et al., 2013; Guevara et al., 2022; Harrison et al., 2020; Jones et al., 2025; Meredith & Kang, 2006; Osier et al., 2015; Pinkernell et al., 2016; Sadler et al., 2022; Shepherd et al., 2016; Wahl et al., 2017; Webster et al., 2014; Xiong et al., 2013; Zarruk et al., 2011
Imaging	97	head-mounted three-photon miniscope; optical intrinsic signal; zte-cbv fmri	Jones et al., 2025; Just, 2021; Markicevic et al., 2021; Osier et al., 2015; Tremoleda & Sosabowski, 2015; Waerzeggers et al., 2010
Histology	74	frozen section; polarizing microscopy; sirius red	Alturkistani et al., 2016; Gurina & Simms, 2025; Javaeed et al., 2021; Jones et al., 2025; Mark et al., 2007; Markicevic et al., 2021; Musumeci, 2014; Osier et al., 2015
Physiology	62	adrenal weight; single-unit extracellular recording; thymus weight	Alemán et al., 2000; Burrows et al., 2019; Guevara et al., 2022; Markicevic et al., 2021; Osier et al., 2015; Pinkernell et al., 2016; Wickenden, 2000

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Use Cases

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

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Installation

pip install assay-extract

Or from source:

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

Quick Start

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

python -m pytest assay_extract/tests/ -v

All 9 tests pass.

License

MIT License

Citation

@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

Acikgoz et al. 2022

Acikgoz, B., Dalkiran, B., & Dayi, A. (2022). An overview of the currency and usefulness of behavioral tests used from past to present to assess anxiety, social behavior and depression in rats and mice. Behavioural Processes, 200, 104670. https://doi.org/10.1016/j.beproc.2022.104670

Adil et al. 2021

Adil, A., Kumar, V., Jan, A. T., & Asger, M. (2021). Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis. Frontiers in Neuroscience, 15. https://doi.org/10.3389/fnins.2021.591122

Alemán et al. 2000

Alemán, C. L., Noa, M., Más, R., Rodeiro, I., Mesa, R., Menéndez, R., Gámez, R., & Hernández, C. (2000). Reference data for the principal physiological indicators in three species of laboratory animals. Laboratory Animals, 34(4), 379–385. https://doi.org/10.1258/002367700780387741

Alturkistani et al. 2016

Alturkistani, H. A., Tashkandi, F. M., & Mohammedsaleh, Z. M. (2016). Histological Stains: A Literature Review and Case Study. Global Journal of Health Science, 8(3), 72–79. https://doi.org/10.5539/gjhs.v8n3p72

Burrows et al. 2019

Burrows, D. J., McGown, A., Jain, S. A., De Felice, M., Ramesh, T. M., Sharrack, B., & Majid, A. (2019). Animal Models of Multiple Sclerosis: From Rodents to Zebrafish. Multiple Sclerosis Journal, 25(3), 306–324. https://doi.org/10.1177/1352458518805246

Chen et al. 2023

Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G.-P. (2023). Applications of multi-omics analysis in human diseases. MedComm, 4(4), e315. https://doi.org/10.1002/mco2.315

Choi & Kumar 2024

Choi, J. D., & Kumar, V. (2024). A new era in quantification of animal social behaviors. Neuroscience & Biobehavioral Reviews, 157, 105528. https://doi.org/10.1016/j.neubiorev.2023.105528

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Dufva, M. (2009). Introduction to Microarray Technology. In M. Dufva (Ed.), DNA Microarrays for Biomedical Research: Methods and Protocols (pp. 1–22). Humana Press. https://doi.org/10.1007/978-1-59745-538-1_1

Gold et al. 2013

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Gregory, N. S., Harris, A. L., Robinson, C. R., Dougherty, P. M., Fuchs, P. N., & Sluka, K. A. (2013). An Overview of Animal Models of Pain: Disease Models and Outcome Measures. The Journal of Pain, 14(11), 1255–1269. https://doi.org/10.1016/j.jpain.2013.06.008

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Javaeed, A., Qamar, S., Ali, S., Mustafa, M. A. T., Nusrat, A., & Ghauri, S. K. (2021). Histological Stains in the Past, Present, and Future. Cureus. https://doi.org/10.7759/cureus.18486

Jin & Kennedy 2015

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Jones et al. 2025

Jones, L. A. T., Field-Fote, E. C., Magnuson, D., Tom, V., Basso, D. M., Fouad, K., & Mulcahey, M. J. (2025). Outcome measures in rodent models for spinal cord injury and their human correlates. Experimental Neurology, 386, 115169. https://doi.org/10.1016/j.expneurol.2025.115169

Just 2021

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