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Testing the reproducibility and robustness of the cancer biology literature by robot

Authors
  • Roper, Katherine
  • Abdel-Rehim, A.
  • Hubbard, Sonya
  • Carpenter, Martin
  • Rzhetsky, Andrey
  • Soldatova, Larisa
  • King, Ross D.
Type
Published Article
Journal
Journal of The Royal Society Interface
Publisher
The Royal Society
Publication Date
Apr 06, 2022
Volume
19
Issue
189
Identifiers
DOI: 10.1098/rsif.2021.0821
PMID: 35382578
PMCID: PMC8984295
Source
PubMed Central
Keywords
Disciplines
  • Research Articles
License
Unknown

Abstract

Scientific results should not just be ‘repeatable’ (replicable in the same laboratory under identical conditions), but also ‘reproducible’ (replicable in other laboratories under similar conditions). Results should also, if possible, be ‘robust’ (replicable under a wide range of conditions). The reproducibility and robustness of only a small fraction of published biomedical results has been tested; furthermore, when reproducibility is tested, it is often not found. This situation is termed ‘the reproducibility crisis', and it is one the most important issues facing biomedicine. This crisis would be solved if it were possible to automate reproducibility testing. Here, we describe the semi-automated testing for reproducibility and robustness of simple statements (propositions) about cancer cell biology automatically extracted from the literature. From 12 260 papers, we automatically extracted statements predicted to describe experimental results regarding a change of gene expression in response to drug treatment in breast cancer, from these we selected 74 statements of high biomedical interest. To test the reproducibility of these statements, two different teams used the laboratory automation system Eve and two breast cancer cell lines (MCF7 and MDA-MB-231). Statistically significant evidence for repeatability was found for 43 statements, and significant evidence for reproducibility/robustness in 22 statements. In two cases, the automation made serendipitous discoveries. The reproduced/robust knowledge provides significant insight into cancer. We conclude that semi-automated reproducibility testing is currently achievable, that it could be scaled up to generate a substantive source of reliable knowledge and that automation has the potential to mitigate the reproducibility crisis.

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