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Type 2 diabetes genetic association database manually curated for the study design and odds ratio

Overview of attention for article published in BMC Medical Informatics and Decision Making, December 2010
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Title
Type 2 diabetes genetic association database manually curated for the study design and odds ratio
Published in
BMC Medical Informatics and Decision Making, December 2010
DOI 10.1186/1472-6947-10-76
Pubmed ID
Authors

Ji Eun Lim, Kyung-Won Hong, Hyun-Seok Jin, Yang Seok Kim, Hun Kuk Park, Bermseok Oh

Abstract

The prevalence of type 2 diabetes has reached epidemic proportions worldwide, and the incidence of life-threatening complications of diabetes through continued exposure of tissues to high glucose levels is increasing. Advances in genotyping technology have increased the scale and accuracy of the genotype data so that an association genetic study has expanded enormously. Consequently, it is difficult to search the published association data efficiently, and several databases on the association results have been constructed, but these databases have their limitations to researchers: some providing only genome-wide association data, some not focused on the association but more on the integrative data, and some are not user-friendly. In this study, a user-friend database of type 2 diabetes genetic association of manually curated information was constructed.

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Mexico 1 3%
Netherlands 1 3%
Brazil 1 3%
Unknown 29 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 21%
Student > Ph. D. Student 6 18%
Student > Master 4 12%
Student > Bachelor 3 9%
Professor 2 6%
Other 9 27%
Unknown 2 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 33%
Medicine and Dentistry 8 24%
Biochemistry, Genetics and Molecular Biology 5 15%
Computer Science 2 6%
Arts and Humanities 2 6%
Other 2 6%
Unknown 3 9%
Attention Score in Context

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 29 August 2013.
All research outputs
#17,695,202
of 22,719,618 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,497
of 1,982 outputs
Outputs of similar age
#158,818
of 180,549 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 6 outputs
Altmetric has tracked 22,719,618 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 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 180,549 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.