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FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach

Overview of attention for article published in BMC Bioinformatics, November 2011
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Title
FEPI-MB: identifying SNPs-disease association using a Markov Blanket-based approach
Published in
BMC Bioinformatics, November 2011
DOI 10.1186/1471-2105-12-s12-s3
Pubmed ID
Authors

Bing Han, Xue-wen Chen, Zohreh Talebizadeh

Abstract

The interactions among genetic factors related to diseases are called epistasis. With the availability of genotyped data from genome-wide association studies, it is now possible to computationally unravel epistasis related to the susceptibility to common complex human diseases such as asthma, diabetes, and hypertension. However, the difficulties of detecting epistatic interaction arose from the large number of genetic factors and the enormous size of possible combinations of genetic factors. Most computational methods to detect epistatic interactions are predictor-based methods and can not find true causal factor elements. Moreover, they are both time-consuming and sample-consuming.

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The data shown below were collected from the profile of 1 X user 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 6%
Unknown 34 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 33%
Researcher 7 19%
Student > Master 3 8%
Student > Doctoral Student 1 3%
Student > Bachelor 1 3%
Other 4 11%
Unknown 8 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 25%
Engineering 5 14%
Computer Science 5 14%
Biochemistry, Genetics and Molecular Biology 3 8%
Mathematics 2 6%
Other 4 11%
Unknown 8 22%
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 12 April 2012.
All research outputs
#18,305,445
of 22,664,267 outputs
Outputs from BMC Bioinformatics
#6,283
of 7,247 outputs
Outputs of similar age
#195,466
of 239,484 outputs
Outputs of similar age from BMC Bioinformatics
#97
of 111 outputs
Altmetric has tracked 22,664,267 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.