↓ Skip to main content

Michigan Publishing

Article Metrics

The development of non-coding RNA ontology

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

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
8 Dimensions

Readers on

mendeley
9 Mendeley
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 profile of 1 tweeter 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Professor 3 33%
Professor > Associate Professor 3 33%
Student > Ph. D. Student 1 11%
Unknown 2 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 33%
Business, Management and Accounting 1 11%
Philosophy 1 11%
Agricultural and Biological Sciences 1 11%
Computer Science 1 11%
Other 1 11%
Unknown 1 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 29 March 2020.
All research outputs
#12,869,818
of 14,571,155 outputs
Outputs from International Journal of Data Mining and Bioinformatics
#62
of 80 outputs
Outputs of similar age
#306,745
of 362,020 outputs
Outputs of similar age from International Journal of Data Mining and Bioinformatics
#2
of 2 outputs
Altmetric has tracked 14,571,155 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 80 research outputs from this source. They receive a mean Attention Score of 2.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 362,020 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
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.