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X Demographics
Mendeley readers
Attention Score in Context
| Title |
TRI_tool: a web-tool for prediction of protein–protein interactions in human transcriptional regulation
|
|---|---|
| Published in |
Bioinformatics, September 2016
|
| DOI | 10.1093/bioinformatics/btw590 |
| Pubmed ID | |
| Authors |
Vladimir Perovic, Neven Sumonja, Branislava Gemovic, Eneda Toska, Stefan G Roberts, Nevena Veljkovic |
| Abstract |
The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions. http://www.vin.bg.ac.rs/180/tools/tfpred.php CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 31% |
| Student > Bachelor | 4 | 14% |
| Student > Ph. D. Student | 4 | 14% |
| Student > Postgraduate | 3 | 10% |
| Student > Doctoral Student | 2 | 7% |
| Other | 3 | 10% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 9 | 31% |
| Agricultural and Biological Sciences | 7 | 24% |
| Computer Science | 2 | 7% |
| Medicine and Dentistry | 2 | 7% |
| Social Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 7 | 24% |
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 08 September 2016.
All research outputs
#18,596,650
of 23,035,022 outputs
Outputs from Bioinformatics
#3,039
of 3,396 outputs
Outputs of similar age
#256,762
of 335,595 outputs
Outputs of similar age from Bioinformatics
#165
of 180 outputs
Altmetric has tracked 23,035,022 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,396 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. This one is in the 4th percentile – i.e., 4% 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 335,595 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 180 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.