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CLO: The cell line ontology

Overview of attention for article published in Journal of Biomedical Semantics, August 2014
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  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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2 X users
wikipedia
1 Wikipedia page
googleplus
1 Google+ user

Citations

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101 Dimensions

Readers on

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74 Mendeley
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3 CiteULike
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Title
CLO: The cell line ontology
Published in
Journal of Biomedical Semantics, August 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.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 1%
Brazil 1 1%
Ukraine 1 1%
Spain 1 1%
Japan 1 1%
United States 1 1%
Unknown 68 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 18 24%
Student > Ph. D. Student 13 18%
Other 9 12%
Student > Bachelor 7 9%
Student > Doctoral Student 4 5%
Other 13 18%
Unknown 10 14%
Readers by discipline Count As %
Computer Science 19 26%
Agricultural and Biological Sciences 15 20%
Biochemistry, Genetics and Molecular Biology 14 19%
Medicine and Dentistry 3 4%
Chemistry 3 4%
Other 9 12%
Unknown 11 15%
Attention Score in Context

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
#6,760,834
of 25,394,764 outputs
Outputs from Journal of Biomedical Semantics
#96
of 368 outputs
Outputs of similar age
#59,611
of 243,173 outputs
Outputs of similar age from Journal of Biomedical Semantics
#7
of 14 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 71% 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 243,173 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 74% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.