This document discusses using text mining and artificial intelligence techniques to identify scientific literature related to the 3Rs (Replacement, Reduction, Refinement) of animal testing. It notes the challenges in manually searching for such literature due to a lack of standardized keywords. The proposed solution involves using a knowledge map and relationship mining of over 500,000 concepts and 200 million relations to connect papers to 3R-related words. A machine learning model is then trained on known 3R and non-3R papers to automatically classify new papers and help identify additional 3R-focused literature. The approach provides a proof of principle for an intelligent search engine to more efficiently find 3R methods papers.

1. Trawling the bibliome
Wynand Alkema
WC11 Maastricht 25-08-2021
For 3R related research

2. Protection of animals used for scientific purposes
– Legislation to protect experimental animals
since 1986 (harmonisation of internal market)
– Directive 2010/63/EU:
• Harmonise practices
• Implement 3Rs and improve animal
welfare
• Improve transparency and enforcement

3. Main findings from a survey:
No budget/time for specific 3R search
Existing relevant 3R information not found / not used
The problem
• How can we identify articles that describe methods
for Replacement, Refinement and Reduction?
– Often not explicitly described as such.
– Hard to define the correct search terms.
• As a consequence the search is
– Elaborate
– Time consuming
– Biased
– Incomplete
3R

4. Solution : Mine the bibliome

5. A Boolean search query
• Dependent on exact keywords
used.
• Even a very restrictive query yields
10s to 100s of papers.
• How to rank and prioritize?
• How to identify 3-R methods?

6. TenWise Knowledge map (KMAP)
> 0.5 million biological concepts Knowledge Map
Large scale text mining
Abstracts Full text
Computer assisted Knowledge map
generation
> 200 million biological relations
Connected to underlying literature

7. How are concepts connected?

8. Automated relationship mining

9. Filtering on 3R related words
https://kmine.tenwiseapps.nl/

10. The problem : Which keywords?
de Vries R and P Whaley. In Vitro Critical Appraisal Tool (IV-CAT): tool development
protocol (1.0.0). Zenodo 2018, DOI: 10.5281/zenodo.1493498.

11. Ordering and ranking the literature
Can we build in intelligent search engine for 3R
papers, based on PubMed?

12. Machine Learning Approach
Based on a set of known
positive and negative papers,
a computer model is trained
that can predict whether a
paper is about a 3R subject.
3865 papers describing
Alternative Animal Tests (Mesh
term)
A random abstract set

13. A computer generated concept map

14. Proof of Principle

15. List of top scoring hits with the model

16. Examine top scoring hits

17. Future steps
Alternative models

18. Conclusions & Acknowledgments
Automated, high-throughput
literature mining combined
with AI based classification can
help in identifying the 3R
needles in the literature
haystack.
Acknowledgements
• Merel Ristkes Hoitinga (SYRCLE, Netherlands)
• Rob de Vries (SYRCLE, Netherlands)
• Judith van Luijk (SYRCLE, Netherlands)
• Brun Ulfhake (Karolinska Institute, Sweden)
• Rafael Frias (Karolinska Institute, Sweden)
• Jennifer Stone (ANU, Australia)
• Brett Lidbury (ANU, Australia)
• Nils Hijlkema (TenWise)