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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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1 tweeter

Citations

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30 Mendeley
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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.

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

Geographical breakdown

Country Count As %
United States 2 7%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 33%
Researcher 7 23%
Student > Master 3 10%
Professor 1 3%
Student > Bachelor 1 3%
Other 4 13%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 30%
Computer Science 5 17%
Biochemistry, Genetics and Molecular Biology 3 10%
Engineering 3 10%
Mathematics 2 7%
Other 3 10%
Unknown 5 17%

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
#9,906,144
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#3,816
of 4,576 outputs
Outputs of similar age
#83,131
of 117,620 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 60 outputs
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