Menagerie: A text-mining tool to support animal-human translation in neurodegeneration research

Zeiss, Caroline J.; Shin, Dongwook; Wyk, Brent Vander; Beck, Amanda P.; Zatz, Natalie; Sneiderman, Charles; Kilicoglu, Halil

doi:10.1371/journal.pone.0226176

Cited by 10 publications

(9 citation statements)

References 83 publications

(142 reference statements)

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“…Annotation and extraction of key statements has also been considered for other publication types, such as pre-clinical animal studies and case reports [39][40][41][42]. One work that is particularly relevant to ours is SciScore [42], a tool which assesses life sciences articles for rigor and transparency, by extracting character-istics including subject's sex, sample size calculation, institutional review board statements, antibodies and cell lines, and calculating a score based on them.…”

Section: Related Workmentioning

confidence: 99%

Toward assessing clinical trial publications for reporting transparency

Kilicoglu

Rosemblat

Hoang

et al. 2021

Journal of Biomedical Informatics

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Section: Related Workmentioning

confidence: 99%

Toward assessing clinical trial publications for reporting transparency

Kilicoglu

Rosemblat

Hoang

et al. 2021

Journal of Biomedical Informatics

Self Cite

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“…Similar approaches have been leveraged to extract specific information—such as the study population, intervention, outcome measured and risks of bias—from abstracts (19) or full texts (17, 20). Bahor and colleagues developed a text mining function in a literature body of stroke animal models able to extract certain risk of bias items including randomization, blinding, and sample size calculation (21).…”

Section: Discussionmentioning

confidence: 99%

Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items fromin vivopublications

Zürrer

Cannon

Ewing

et al. 2023

Preprint

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Background: Systematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain systematic review steps such as data extraction are labour-intensive which hampers their applicability, not least with the rapidly expanding body of biomedical literature. Objective: To bridge this gap, we aimed at developing a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n=45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n=31 publications; multiple sclerosis, n=244 publications). Results: Our data mining tool Auto-STEED (Automated and STructured Extraction of Experimental Data) was able to extract key experimental parameters such as animal models and species as well as risk of bias items such as randomization or blinding from in vivo studies. Sensitivity and specificity were over 85 and 80%, respectively, for most items in both validation corpora. Accuracy and F-scores were above 90% and 0.9 for most items in the validation corpora. Time savings were above 99%. Conclusions: Our developed text mining tool Auto-STEED is able to extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. With this, the tool can be deployed to probe a field in a research improvement context or to replace one human reader during data extraction resulting in substantial time-savings and contribute towards automation of systematic reviews. The function is available on Github.

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“…In the same vein, if the application of systematic reviews and meta-analyses and the IB-derisk become commonplace, training will become widespread and their execution will be more efficient. The development of automated methods that can compile vast amounts of data will certainly aid to this end [ 45 , 46 ].…”

Section: Levelling the Translational Gap For Animal To Human Efficmentioning

confidence: 99%

Levelling the Translational Gap for Animal to Human Efficacy Data

Ferreira

Veening-Griffioen

Boon

et al. 2020

Animals

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Reports of a reproducibility crisis combined with a high attrition rate in the pharmaceutical industry have put animal research increasingly under scrutiny in the past decade. Many researchers and the general public now question whether there is still a justification for conducting animal studies. While criticism of the current modus operandi in preclinical research is certainly warranted, the data on which these discussions are based are often unreliable. Several initiatives to address the internal validity and reporting quality of animal studies (e.g., Animals in Research: Reporting In Vivo Experiments (ARRIVE) and Planning Research and Experimental Procedures on Animals: Recommendations for Excellence (PREPARE) guidelines) have been introduced but seldom implemented. As for external validity, progress has been virtually absent. Nonetheless, the selection of optimal animal models of disease may prevent the conducting of clinical trials, based on unreliable preclinical data. Here, we discuss three contributions to tackle the evaluation of the predictive value of animal models of disease themselves. First, we developed the Framework to Identify Models of Disease (FIMD), the first step to standardise the assessment, validation and comparison of disease models. FIMD allows the identification of which aspects of the human disease are replicated in the animals, facilitating the selection of disease models more likely to predict human response. Second, we show an example of how systematic reviews and meta-analyses can provide another strategy to discriminate between disease models quantitatively. Third, we explore whether external validity is a factor in animal model selection in the Investigator’s Brochure (IB), and we use the IB-derisk tool to integrate preclinical pharmacokinetic and pharmacodynamic data in early clinical development. Through these contributions, we show how we can address external validity to evaluate the translatability and scientific value of animal models in drug development. However, while these methods have potential, it is the extent of their adoption by the scientific community that will define their impact. By promoting and adopting high quality study design and reporting, as well as a thorough assessment of the translatability of drug efficacy of animal models of disease, we will have robust data to challenge and improve the current animal research paradigm.

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Menagerie: A text-mining tool to support animal-human translation in neurodegeneration research

Cited by 10 publications

References 83 publications

Toward assessing clinical trial publications for reporting transparency

Toward assessing clinical trial publications for reporting transparency

Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items fromin vivopublications

Levelling the Translational Gap for Animal to Human Efficacy Data

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