↓ Skip to main content

HAT: Hypergeometric Analysis of Tiling-arrays with application to promoter-GeneChip data

Overview of attention for article published in BMC Bioinformatics, January 2010
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
7 Dimensions

Readers on

mendeley
29 Mendeley
citeulike
4 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
HAT: Hypergeometric Analysis of Tiling-arrays with application to promoter-GeneChip data
Published in
BMC Bioinformatics, January 2010
DOI 10.1186/1471-2105-11-275
Pubmed ID
Authors

Erdogan Taskesen, Renee Beekman, Jeroen de Ridder, Bas J Wouters, Justine K Peeters, Ivo P Touw, Marcel J.T Reinders, Ruud Delwel

Abstract

Tiling-arrays are applicable to multiple types of biological research questions. Due to its advantages (high sensitivity, resolution, unbiased), the technology is often employed in genome-wide investigations. A major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity (as a result of hybridization of labeled DNA) in a region. Currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width. Furthermore, the chromosomal distance between neighboring probes can vary across the genome among different arrays.

Mendeley readers

The data shown below were compiled from readership statistics for 29 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%
Denmark 1 3%
United States 1 3%
Luxembourg 1 3%
Unknown 24 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 28%
Researcher 8 28%
Professor > Associate Professor 3 10%
Lecturer 2 7%
Other 2 7%
Other 4 14%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 48%
Medicine and Dentistry 4 14%
Computer Science 3 10%
Biochemistry, Genetics and Molecular Biology 2 7%
Unspecified 1 3%
Other 3 10%
Unknown 2 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 06 June 2012.
All research outputs
#817,417
of 3,627,846 outputs
Outputs from BMC Bioinformatics
#788
of 2,289 outputs
Outputs of similar age
#24,660
of 95,051 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 137 outputs
Altmetric has tracked 3,627,846 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 58% 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 95,051 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 72% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.