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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Non-coding yet non-trivial: a review on the computational genomics of lincRNAs
|
|---|---|
| Published in |
BioData Mining, December 2015
|
| DOI | 10.1186/s13040-015-0075-z |
| Pubmed ID | |
| Authors |
Travers Ching, Jayson Masaki, Jason Weirather, Lana X. Garmire |
| Abstract |
Long intergenic non-coding RNAs (lincRNAs) represent one of the most mysterious RNA species encoded by the human genome. Thanks to next generation sequencing (NGS) technology and its applications, we have recently witnessed a surge in non-coding RNA research, including lincRNA research. Here, we summarize the recent advancement in genomics studies of lincRNAs. We review the emerging characteristics of lincRNAs, the experimental and computational approaches to identify lincRNAs, their known mechanisms of regulation, the computational methods and resources for lincRNA functional predictions, and discuss the challenges to understanding lincRNA comprehensively. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 17% |
| United Kingdom | 1 | 17% |
| United States | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Scientists | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| India | 1 | 1% |
| China | 1 | 1% |
| Norway | 1 | 1% |
| Unknown | 62 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 15 | 22% |
| Researcher | 15 | 22% |
| Student > Bachelor | 8 | 12% |
| Student > Master | 7 | 10% |
| Student > Doctoral Student | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 9 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 26 | 39% |
| Biochemistry, Genetics and Molecular Biology | 11 | 16% |
| Computer Science | 7 | 10% |
| Medicine and Dentistry | 5 | 7% |
| Business, Management and Accounting | 2 | 3% |
| Other | 6 | 9% |
| Unknown | 10 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 28 December 2015.
All research outputs
#14,603,323
of 25,706,302 outputs
Outputs from BioData Mining
#181
of 325 outputs
Outputs of similar age
#189,514
of 398,350 outputs
Outputs of similar age from BioData Mining
#10
of 21 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 325 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 43rd percentile – i.e., 43% 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 398,350 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 52% of its contemporaries.
We're also able to compare this research output to 21 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 52% of its contemporaries.