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Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels

Overview of attention for article published in BMC Systems Biology, December 2014
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Mentioned by

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2 tweeters

Citations

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9 Dimensions

Readers on

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16 Mendeley
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Title
Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels
Published in
BMC Systems Biology, December 2014
DOI 10.1186/1752-0509-8-s5-s5
Pubmed ID
Authors

Xiaolei Wang, Hiroyuki Kuwahara, Xin Gao

Abstract

A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinity. Importantly, such experiments revealed the complex nature of TF-DNA interactions, whereby the effects of nucleotide changes on the binding affinity were observed to be context dependent. A systematic method to give high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology.

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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 31%
Student > Bachelor 4 25%
Researcher 2 13%
Student > Master 1 6%
Professor > Associate Professor 1 6%
Other 0 0%
Unknown 3 19%
Readers by discipline Count As %
Computer Science 4 25%
Agricultural and Biological Sciences 3 19%
Engineering 2 13%
Mathematics 1 6%
Biochemistry, Genetics and Molecular Biology 1 6%
Other 2 13%
Unknown 3 19%

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 22 January 2015.
All research outputs
#15,748,294
of 20,327,027 outputs
Outputs from BMC Systems Biology
#761
of 1,134 outputs
Outputs of similar age
#210,660
of 314,799 outputs
Outputs of similar age from BMC Systems Biology
#5
of 8 outputs
Altmetric has tracked 20,327,027 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 1,134 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 27th percentile – i.e., 27% 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 314,799 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.