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Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Overview of attention for article published in Algorithms for Molecular Biology, April 2014
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About this Attention Score

  • Among the highest-scoring outputs from this source (#36 of 108)
  • Above-average Attention Score compared to outputs of the same age (56th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

Mentioned by

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1 tweeter
googleplus
1 Google+ user

Citations

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2 Dimensions

Readers on

mendeley
17 Mendeley
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Title
Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions
Published in
Algorithms for Molecular Biology, April 2014
DOI 10.1186/1748-7188-9-10
Pubmed ID
Abstract

The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown.

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

Geographical breakdown

Country Count As %
Germany 1 6%
Unknown 16 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 29%
Student > Master 3 18%
Researcher 3 18%
Other 2 12%
Student > Bachelor 1 6%
Other 1 6%
Unknown 2 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 29%
Biochemistry, Genetics and Molecular Biology 4 24%
Computer Science 4 24%
Economics, Econometrics and Finance 1 6%
Psychology 1 6%
Other 0 0%
Unknown 2 12%

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 07 August 2014.
All research outputs
#1,996,384
of 4,506,837 outputs
Outputs from Algorithms for Molecular Biology
#36
of 108 outputs
Outputs of similar age
#44,171
of 107,021 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 9 outputs
Altmetric has tracked 4,506,837 research outputs across all sources so far. This one has received more attention than most of these and is in the 53rd percentile.
So far Altmetric has tracked 108 research outputs from this source. They receive a mean Attention Score of 2.1. This one has gotten more attention than average, scoring higher than 62% of its peers.
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 107,021 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.