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An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis

Overview of attention for article published in BMC Bioinformatics, March 2013
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1 tweeter

Citations

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64 Dimensions

Readers on

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66 Mendeley
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4 CiteULike
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Title
An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis
Published in
BMC Bioinformatics, March 2013
DOI 10.1186/1471-2105-14-90
Pubmed ID
Authors

Chuanxin Zou, Jiayu Gong, Honglin Li

Abstract

DNA-binding proteins (DNA-BPs) play a pivotal role in both eukaryotic and prokaryotic proteomes. There have been several computational methods proposed in the literature to deal with the DNA-BPs, many informative features and properties were used and proved to have significant impact on this problem. However the ultimate goal of Bioinformatics is to be able to predict the DNA-BPs directly from primary sequence.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
Germany 1 2%
Israel 1 2%
Iran, Islamic Republic of 1 2%
Russia 1 2%
Spain 1 2%
Unknown 59 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 20%
Researcher 11 17%
Professor > Associate Professor 8 12%
Student > Master 8 12%
Student > Bachelor 5 8%
Other 10 15%
Unknown 11 17%
Readers by discipline Count As %
Computer Science 24 36%
Agricultural and Biological Sciences 13 20%
Biochemistry, Genetics and Molecular Biology 5 8%
Engineering 5 8%
Medicine and Dentistry 3 5%
Other 4 6%
Unknown 12 18%

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 09 March 2013.
All research outputs
#18,989,782
of 21,344,814 outputs
Outputs from BMC Bioinformatics
#6,511
of 6,922 outputs
Outputs of similar age
#150,212
of 171,003 outputs
Outputs of similar age from BMC Bioinformatics
#36
of 37 outputs
Altmetric has tracked 21,344,814 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,922 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 1st percentile – i.e., 1% 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 171,003 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.