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ScaPD: a database for human scaffold proteins

Overview of attention for article published in BMC Bioinformatics, October 2017
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Title
ScaPD: a database for human scaffold proteins
Published in
BMC Bioinformatics, October 2017
DOI 10.1186/s12859-017-1806-6
Pubmed ID
Authors

Xiaomei Han, Jenny Wang, Jie Wang, Sheng Liu, Jianfei Hu, Heng Zhu, Jiang Qian

Abstract

Scaffold proteins play a critical role in an increasing number of biological signaling processes, including simple tethering mechanism, regulating selectivity in pathways, shaping cellular behaviors. While many databases document the signaling pathways, few databases are devoted to the scaffold proteins that medicate signal transduction. Here, we have developed a user-friendly database, ScaPD, to describe computationally predicted, experimentally validated scaffold proteins and associated signaling pathways. It currently contains 273 scaffold proteins and 1118 associated signaling pathways. The database allows users to search, navigate and download the scaffold protein-mediated signaling networks. Manually curated and predicted scaffold protein data will be a foundation for further investigation of the scaffold protein in the signal transduction. With maintained up-to-date data, ScaPD ( http://bioinfo.wilmer.jhu.edu/ScaPD ) will be a valuable resource for understanding how individual signaling pathways are regulated.

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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.
Mendeley readers

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 %
Student > Bachelor 5 45%
Student > Master 3 27%
Student > Doctoral Student 1 9%
Student > Ph. D. Student 1 9%
Unknown 1 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 36%
Biochemistry, Genetics and Molecular Biology 3 27%
Immunology and Microbiology 1 9%
Chemistry 1 9%
Unknown 2 18%
Attention Score in Context

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 04 October 2017.
All research outputs
#17,917,778
of 23,005,189 outputs
Outputs from BMC Bioinformatics
#5,965
of 7,312 outputs
Outputs of similar age
#231,058
of 323,064 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 105 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 323,064 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.