An Ontology To Represent Knowledge On Animal Testing Alternatives

Wächter, Thomas; Sauer, Ursula G.; Doms, Andreas; Grune, Barbara; Alvers, Michael R.; Spielmann, Horst; Schroeder, Michael

doi:10.1038/npre.2009.3148.1

Cited by 2 publications

(3 citation statements)

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“…Another important collection of non-animal models is the EU reports that the EU Commission regularly releases for some specific topics. As of January/2022, we are aware of four reports for the areas of breast cancer [6] , respiratory tract diseases [7] , neurodegenerative diseases [8] , and immuno-oncology [9] . The corresponding data for each report can be downloaded as a spreadsheet, and similar to the NAT database, it includes information about the methods or models.…”

Section: Related Workmentioning

confidence: 99%

“…The respective labels used in the first version of our corpus designate materials from vertebrate animals only. Other biological materials, used in experimental biomed- [6] https://op.europa.eu/en/publication-detail/-/publication/8b7b3030-1a64-11eb-b57e-01aa75ed71a1 [7] https://op.europa.eu/en/publication-detail/-/publication/c8ec5086-f988-11ea-b44f-01aa75ed71a1 [8] https://op.europa.eu/en/publication-detail/-/publication/5d9512e7-a89b-11eb-9585-01aa75ed71a1 [9] https://op.europa.eu/en/publication-detail/-/publication/b50a15b5-00ff-11ec-8f47-01aa75ed71a1 ical research, are labeled as "human" or "invertebrate", depending on the source.…”

Section: Related Workmentioning

confidence: 99%

“…In one previous attempt, our group developed an ontology for this field [7], to support the Go3R search engine [8]. Go3R was one of the first attempts to develop a tool for finding alternative methods to animal experiments.…”

Section: Related Workmentioning

confidence: 99%

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Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments

Neves

Klippert

Knöspel

et al. 2022

Preprint

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Background: European Union legislature requires replacement of animal experiments with alternative methods, whenever such methods are suitable to reach the intended scientific objective. However, searching for alternative methods in the scientific literature is a time-consuming task that requires careful screening of an enormously large number of experimental biomedical publications. The identification of potentially relevant methods, e.g. organ or cell culture models, or computer simulations, can be supported with text mining tools specifically built for this purpose. Such tools are trained (or fine tuned) on relevant data sets labeled by human experts. Methods: We developed the GoldHamster corpus, composed of 1,600 PubMed (Medline) abstracts, in which we manually identified the used experimental model according to a set of eight labels, namely: “in vivo”, “organs”, “primary cells”, “immortal cell lines”, “invertebrates”, “humans”, “in silico” and “other” (models). We recruited 13 annotators with expertise in the biomedical domain and assigned each article to two individuals. Three additional rounds of annotation aimed at improving the quality of the annotations with disagreements in the first round. Furthermore, we conducted various machine learning experiments based on supervised learning to evaluate the suitability of the corpus for our classification task. Results: We obtained more than 7,000 abstract-level annotations for the above labels. The inter-annotator agreement (kappa coefficient) varied among labels, and ranged from 0.63 (for “others”) to 0.82 (for “invertebrates”), with an overall score of 0.74. The best-performing machine learning experiment used the BioBERT pre-trained model with fine-tuning to our corpus, which gained an overall f-score of 0.82. Conclusions: We obtained a high agreement for most of the labels, and our evaluation demonstrated, that our corpus is suitable for training reliable predictive models for automatic classification of biomedical literature according to the used experimental models. Our “Smart feature-based interactive” search tool (SMAFIRA) will employ this classifier for supporting the retrieval of alternative methods to animal experiments. The corpus and the source code will be made available.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments

Neves

Klippert

Knöspel

et al. 2022

Preprint

Self Cite

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show abstract

Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments

Neves,

Klippert,

Knöspel

et al. 2023

J Biomed Semant

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Current animal protection laws require replacement of animal experiments with alternative methods, whenever such methods are suitable to reach the intended scientific objective. However, searching for alternative methods in the scientific literature is a time-consuming task that requires careful screening of an enormously large number of experimental biomedical publications. The identification of potentially relevant methods, e.g. organ or cell culture models, or computer simulations, can be supported with text mining tools specifically built for this purpose. Such tools are trained (or fine tuned) on relevant data sets labeled by human experts. We developed the GoldHamster corpus, composed of 1,600 PubMed (Medline) articles (titles and abstracts), in which we manually identified the used experimental model according to a set of eight labels, namely: “in vivo”, “organs”, “primary cells”, “immortal cell lines”, “invertebrates”, “humans”, “in silico” and “other” (models). We recruited 13 annotators with expertise in the biomedical domain and assigned each article to two individuals. Four additional rounds of annotation aimed at improving the quality of the annotations with disagreements in the first round. Furthermore, we conducted various machine learning experiments based on supervised learning to evaluate the corpus for our classification task. We obtained more than 7,000 document-level annotations for the above labels. After the first round of annotation, the inter-annotator agreement (kappa coefficient) varied among labels, and ranged from 0.42 (for “others”) to 0.82 (for “invertebrates”), with an overall score of 0.62. All disagreements were resolved in the subsequent rounds of annotation. The best-performing machine learning experiment used the PubMedBERT pre-trained model with fine-tuning to our corpus, which gained an overall f-score of 0.83. We obtained a corpus with high agreement for all labels, and our evaluation demonstrated that our corpus is suitable for training reliable predictive models for automatic classification of biomedical literature according to the used experimental models. Our SMAFIRA - “Smart feature-based interactive” - search tool (https://smafira.bf3r.de) will employ this classifier for supporting the retrieval of alternative methods to animal experiments. The corpus is available for download (https://doi.org/10.5281/zenodo.7152295), as well as the source code (https://github.com/mariananeves/goldhamster) and the model (https://huggingface.co/SMAFIRA/goldhamster).

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An Ontology To Represent Knowledge On Animal Testing Alternatives

Cited by 2 publications

References 0 publications

Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments

Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments

Automatic classification of experimental models in biomedical literature to support searching for alternative methods to animal experiments

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