---
title: "Automated Screening of Antibacterial Nanoparticle Literature:"
description: "The authors tackle the problem of finding, within a fast-growing body of nanomedicine papers, those studies where an… 29 references."
authors: ["Alperen Ozturk", "Şaziye Betül Özateş", "Sophia Bahar Root", "Angela Violi", "Nicholas Kotov", "J. Scott VanEpps", "Emine Sumeyra Turali Emre"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.20/"
doi: "10.18653/v1/2026.eacl-long.20"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ozturk-2026-automated-screening-antibacterial-nanoparticle-literature/"
---
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# Automated Screening of Antibacterial Nanoparticle Literature: Dataset Curation and Model Evaluation

**Authors:** Alperen Ozturk, [Şaziye Betül Özateş](https://md.research.elamur.ai/free/en/authors/saziye-betul-ozates/), Sophia Bahar Root, Angela Violi, Nicholas Kotov, J. Scott VanEpps and Emine Sumeyra Turali Emre · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.20/) · [PDF](https://aclanthology.org/2026.eacl-long.20.pdf) · [DOI 10.18653/v1/2026.eacl-long.20](https://doi.org/10.18653/v1/2026.eacl-long.20) · [OpenAlex](https://openalex.org/W7140120855)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The authors tackle the problem of finding, within a fast-growing body of nanomedicine papers, those studies where an inorganic nanoparticle kills bacteria by itself. This excludes cases where it only carries a drug or sits on a coated surface. They frame this as binary include/exclude classification of titles and abstracts. They built AINA, a corpus of 7,910 manually labelled MEDLINE articles. Labelling followed a mechanism-oriented rubric that requires synthesis, characterization and antibacterial testing of the particle. Traditional machine-learning classifiers were compared with deep learning models. A fine-tuned BioBERT performed best (macro F1 0.82). A TF-IDF + SVM baseline came fairly close, which suggests that cheap models remain useful when resources are limited. Error analysis shows the two classes share vocabulary such as "nanoparticle" and "antibacterial", so separating them depends on context rather than keywords. The resource matters because it can support systematic reviews, structured databases of nanoparticle properties, and models that predict antibacterial efficacy from physicochemical features.

## Key points
- **Corpus construction:** over 300,000 nanoparticle papers were retrieved from MEDLINE (via OVID). About 10,000 were screened and roughly 8,000 annotated, giving the final AINA set of 7,910 articles.
- **Splits:**
  - Training: 7,336 papers labelled by one annotator (2,233 include / 5,103 exclude).
  - Test: 574 papers with two-annotator consensus labels (204 include / 370 exclude).
  - The exclude-to-include ratio is about 2.25:1.
- **Exclusion rules:** the rubric rejects photocatalysis, nanofibers, liposomes, engineered peptides, and particles larger than 1 µm in any dimension.
- **Results:** BioBERT reached macro F1 0.82, and TF-IDF + SVM reached 0.78. Stated limitations include single-annotator noise, use of abstracts only, and class imbalance.
- **Release:** labels, DOIs and the best models are on GitHub (alperenmo/AINA_Dataset). Abstract texts are not included for licensing reasons, but a script for fetching them is provided.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Alperen Ozturk, Şaziye Betül Özateş, Sophia Bahar Root, Angela Violi, Nicholas Kotov, J. Scott VanEpps and Emine Sumeyra Turali Emre. 2026. Automated Screening of Antibacterial Nanoparticle Literature: Dataset Curation and Model Evaluation. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ozturk2026automated,
  title = {{Automated Screening of Antibacterial Nanoparticle Literature: Dataset Curation and Model Evaluation}},
  author = {Alperen Ozturk, Şaziye Betül Özateş, Sophia Bahar Root, Angela Violi, Nicholas Kotov, J. Scott VanEpps and Emine Sumeyra Turali Emre},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.20},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.20}
}
```

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*Updated 1 November 2026*