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Efficient techniques for genotype‐phenotype correlational analysis

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2013
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Title
Efficient techniques for genotype‐phenotype correlational analysis
Published in
BMC Medical Informatics and Decision Making, April 2013
DOI 10.1186/1472-6947-13-41
Pubmed ID
Authors

Subrata Saha, Sanguthevar Rajasekaran, Jinbo Bi, Sudipta Pathak

Abstract

Single Nucleotide Polymorphisms (SNPs) are sequence variations found in individuals at some specific points in the genomic sequence. As SNPs are highly conserved throughout evolution and within a population, the map of SNPs serves as an excellent genotypic marker. Conventional SNPs analysis mechanisms suffer from large run times, inefficient memory usage, and frequent overestimation. In this paper, we propose efficient, scalable, and reliable algorithms to select a small subset of SNPs from a large set of SNPs which can together be employed to perform phenotypic classification.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 13%
United States 1 7%
India 1 7%
Unknown 11 73%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 40%
Other 3 20%
Student > Master 3 20%
Researcher 2 13%
Professor > Associate Professor 1 7%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 33%
Computer Science 5 33%
Biochemistry, Genetics and Molecular Biology 2 13%
Mathematics 1 7%
Environmental Science 1 7%
Other 1 7%
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 23 April 2013.
All research outputs
#16,050,520
of 25,390,970 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,180
of 2,139 outputs
Outputs of similar age
#126,096
of 213,728 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#26
of 38 outputs
Altmetric has tracked 25,390,970 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,139 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 39th percentile – i.e., 39% 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 213,728 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 38 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.