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The development of non-coding RNA ontology

Overview of attention for article published in International Journal of Data Mining and Bioinformatics, January 2016
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Mentioned by

twitter
3 tweeters

Citations

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8 Dimensions

Readers on

mendeley
11 Mendeley
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Title
The development of non-coding RNA ontology
Published in
International Journal of Data Mining and Bioinformatics, January 2016
DOI 10.1504/ijdmb.2016.077072
Pubmed ID
Authors

Jingshan Huang, Karen Eilbeck, Barry Smith, Judith A. Blake, Dejing Dou, Weili Huang, Darren A. Natale, Alan Ruttenberg, Jun Huan, Michael T. Zimmermann, Guoqian Jiang, Yu Lin, Bin Wu, Harrison J. Strachan, Nisansa De Silva, Mohan Vamsi Kasukurthi, Vikash Kumar Jha, Yongqun He, Shaojie Zhang, Xiaowei Wang, Zixing Liu, Glen M. Borchert, Ming Tan

Abstract

Identification of non-coding RNAs (ncRNAs) has been significantly improved over the past decade. On the other hand, semantic annotation of ncRNA data is facing critical challenges due to the lack of a comprehensive ontology to serve as common data elements and data exchange standards in the field. We developed the Non-Coding RNA Ontology (NCRO) to handle this situation. By providing a formally defined ncRNA controlled vocabulary, the NCRO aims to fill a specific and highly needed niche in semantic annotation of large amounts of ncRNA biological and clinical data.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 27%
Professor > Associate Professor 3 27%
Student > Ph. D. Student 1 9%
Researcher 1 9%
Unknown 3 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 36%
Agricultural and Biological Sciences 1 9%
Philosophy 1 9%
Computer Science 1 9%
Medicine and Dentistry 1 9%
Other 0 0%
Unknown 3 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 21 October 2021.
All research outputs
#11,507,229
of 18,998,919 outputs
Outputs from International Journal of Data Mining and Bioinformatics
#36
of 90 outputs
Outputs of similar age
#198,549
of 385,688 outputs
Outputs of similar age from International Journal of Data Mining and Bioinformatics
#1
of 1 outputs
Altmetric has tracked 18,998,919 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 90 research outputs from this source. They receive a mean Attention Score of 2.3. This one has gotten more attention than average, scoring higher than 58% 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 385,688 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 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