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SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS

Overview of attention for article published in BMC Bioinformatics, January 2013
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Mentioned by

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3 X users

Citations

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25 Dimensions

Readers on

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111 Mendeley
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2 CiteULike
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Title
SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS
Published in
BMC Bioinformatics, January 2013
DOI 10.1186/1471-2105-14-s1-s9
Pubmed ID
Authors

Ivan Merelli, Andrea Calabria, Paolo Cozzi, Federica Viti, Ettore Mosca, Luciano Milanesi

Abstract

The capability of correlating specific genotypes with human diseases is a complex issue in spite of all advantages arisen from high-throughput technologies, such as Genome Wide Association Studies (GWAS). New tools for genetic variants interpretation and for Single Nucleotide Polymorphisms (SNPs) prioritization are actually needed. Given a list of the most relevant SNPs statistically associated to a specific pathology as result of a genotype study, a critical issue is the identification of genes that are effectively related to the disease by re-scoring the importance of the identified genetic variations. Vice versa, given a list of genes, it can be of great importance to predict which SNPs can be involved in the onset of a particular disease, in order to focus the research on their effects.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 3%
Germany 1 <1%
Netherlands 1 <1%
Sweden 1 <1%
Brazil 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 102 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 24%
Student > Ph. D. Student 22 20%
Student > Master 13 12%
Student > Postgraduate 10 9%
Student > Bachelor 8 7%
Other 16 14%
Unknown 15 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 29%
Biochemistry, Genetics and Molecular Biology 23 21%
Computer Science 15 14%
Medicine and Dentistry 10 9%
Psychology 3 3%
Other 8 7%
Unknown 20 18%
Attention Score in Context

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 05 March 2013.
All research outputs
#14,162,589
of 22,696,971 outputs
Outputs from BMC Bioinformatics
#4,714
of 7,254 outputs
Outputs of similar age
#169,494
of 283,936 outputs
Outputs of similar age from BMC Bioinformatics
#82
of 138 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 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 30th percentile – i.e., 30% 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 283,936 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.