Usage of cell nomenclature in biomedical literature

Şenay Kafkas*, Sirarat Sarntivijai, Robert Hoehndorf

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background: Cell lines and cell types are extensively studied in biomedical research yielding to a significant amount of publications each year. Identifying cell lines and cell types precisely in publications is crucial for science reproducibility and knowledge integration. There are efforts for standardisation of the cell nomenclature based on ontology development to support FAIR principles of the cell knowledge. However, it is important to analyse the usage of cell nomenclature in publications at a large scale for understanding the level of uptake of cell nomenclature in literature by scientists. In this study, we analyse the usage of cell nomenclature, both in Vivo, and in Vitro in biomedical literature by using text mining methods and present our results. Results: We identified 59% of the cell type classes in the Cell Ontology and 13% of the cell line classes in the Cell Line Ontology in the literature. Our analysis showed that cell line nomenclature is much more ambiguous compared to the cell type nomenclature. However, trends indicate that standardised nomenclature for cell lines and cell types are being increasingly used in publications by the scientists. Conclusions: Our findings provide an insight to understand how experimental cells are described in publications and may allow for an improved standardisation of cell type and cell line nomenclature as well as can be utilised to develop efficient text mining applications on cell types and cell lines. All data generated in this study is available at https://github.com/shenay/CellNomenclatureStudy.

Original languageEnglish (US)
Article number561
JournalBMC BIOINFORMATICS
Volume18
DOIs
StatePublished - Dec 21 2017

Bibliographical note

Publisher Copyright:
© 2017 The Author(s).

Keywords

  • Cell lines
  • Cell nomenclature
  • Cell types
  • Ontologies
  • Text mining

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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