↓ Skip to main content

CLO: The cell line ontology

Overview of attention for article published in Journal of Biomedical Semantics, January 2014
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)

Mentioned by

twitter
2 tweeters
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

dimensions_citation
69 Dimensions

Readers on

mendeley
63 Mendeley
citeulike
3 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
CLO: The cell line ontology
Published in
Journal of Biomedical Semantics, January 2014
DOI 10.1186/2041-1480-5-37
Pubmed ID
Authors

Sirarat Sarntivijai, Yu Lin, Zuoshuang Xiang, Terrence F Meehan, Alexander D Diehl, Uma D Vempati, Stephan C Schürer, Chao Pang, James Malone, Helen Parkinson, Yue Liu, Terue Takatsuki, Kaoru Saijo, Hiroshi Masuya, Yukio Nakamura, Matthew H Brush, Melissa A Haendel, Jie Zheng, Christian J Stoeckert, Bjoern Peters, Christopher J Mungall, Thomas E Carey, David J States, Brian D Athey, Yongqun He

Abstract

Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions. Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as 'cell line', 'cell line cell', 'cell line culturing', and 'mortal' vs. 'immortal cell line cell'. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms. The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLO's utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Brazil 1 2%
Ukraine 1 2%
Spain 1 2%
Japan 1 2%
United States 1 2%
Unknown 57 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 25%
Student > Ph. D. Student 10 16%
Other 8 13%
Student > Bachelor 6 10%
Student > Postgraduate 4 6%
Other 10 16%
Unknown 9 14%
Readers by discipline Count As %
Computer Science 15 24%
Agricultural and Biological Sciences 14 22%
Biochemistry, Genetics and Molecular Biology 12 19%
Medicine and Dentistry 3 5%
Chemistry 3 5%
Other 6 10%
Unknown 10 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 February 2020.
All research outputs
#4,318,664
of 16,882,949 outputs
Outputs from Journal of Biomedical Semantics
#97
of 351 outputs
Outputs of similar age
#72,115
of 269,219 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 2 outputs
Altmetric has tracked 16,882,949 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 351 research outputs from this source. They receive a mean Attention Score of 4.5. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 269,219 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them