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TRI_tool: a web-tool for prediction of protein–protein interactions in human transcriptional regulation

Overview of attention for article published in Bioinformatics, September 2016
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Mentioned by

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2 tweeters

Citations

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7 Dimensions

Readers on

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23 Mendeley
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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: vladaper@vinca.rs; nevenav@vinca.rs SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 39%
Student > Postgraduate 3 13%
Student > Doctoral Student 3 13%
Student > Bachelor 2 9%
Student > Ph. D. Student 2 9%
Other 3 13%
Unknown 1 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 30%
Agricultural and Biological Sciences 6 26%
Medicine and Dentistry 2 9%
Social Sciences 2 9%
Computer Science 1 4%
Other 2 9%
Unknown 3 13%

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
#11,068,263
of 14,571,953 outputs
Outputs from Bioinformatics
#8,219
of 9,371 outputs
Outputs of similar age
#172,762
of 264,283 outputs
Outputs of similar age from Bioinformatics
#289
of 322 outputs
Altmetric has tracked 14,571,953 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,371 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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We're also able to compare this research output to 322 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.