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EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome

Overview of attention for article published in BMC Genomics, June 2014
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Mentioned by

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3 tweeters

Citations

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18 Dimensions

Readers on

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44 Mendeley
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Title
EFIN: predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome
Published in
BMC Genomics, June 2014
DOI 10.1186/1471-2164-15-455
Pubmed ID
Authors

Shuai Zeng, Jing Yang, Brian Hon-Yin Chung, Yu Lung Lau, Wanling Yang

Abstract

Predicting the functional impact of amino acid substitutions (AAS) caused by nonsynonymous single nucleotide polymorphisms (nsSNPs) is becoming increasingly important as more and more novel variants are being discovered. Bioinformatics analysis is essential to predict potentially causal or contributing AAS to human diseases for further analysis, as for each genome, thousands of rare or private AAS exist and only a very small number of which are related to an underlying disease. Existing algorithms in this field still have high false prediction rate and novel development is needed to take full advantage of vast amount of genomic data.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Spain 1 2%
Sweden 1 2%
Unknown 41 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 34%
Student > Ph. D. Student 9 20%
Student > Master 6 14%
Student > Bachelor 2 5%
Student > Postgraduate 2 5%
Other 5 11%
Unknown 5 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 41%
Biochemistry, Genetics and Molecular Biology 9 20%
Medicine and Dentistry 4 9%
Sports and Recreations 1 2%
Mathematics 1 2%
Other 2 5%
Unknown 9 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 31 January 2015.
All research outputs
#14,781,727
of 22,757,090 outputs
Outputs from BMC Genomics
#6,125
of 10,637 outputs
Outputs of similar age
#127,615
of 229,145 outputs
Outputs of similar age from BMC Genomics
#104
of 210 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,637 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 37th percentile – i.e., 37% 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 229,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.