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Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data

Overview of attention for article published in BioData Mining, March 2011
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

twitter
2 tweeters
patent
1 patent

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
37 Mendeley
citeulike
4 CiteULike
connotea
1 Connotea
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Title
Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data
Published in
BioData Mining, March 2011
DOI 10.1186/1756-0381-4-5
Pubmed ID
Authors

Sami K Kilpinen, Kalle A Ojala, Olli P Kallioniemi

Abstract

Gene expression microarray data have been organized and made available as public databases, but the utilization of such highly heterogeneous reference datasets in the interpretation of data from individual test samples is not as developed as e.g. in the field of nucleotide sequence comparisons. We have created a rapid and powerful approach for the alignment of microarray gene expression profiles (AGEP) from test samples with those contained in a large annotated public reference database and demonstrate here how this can facilitate interpretation of microarray data from individual samples.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Brazil 1 3%
Australia 1 3%
Canada 1 3%
Finland 1 3%
Unknown 31 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 30%
Student > Ph. D. Student 9 24%
Professor 4 11%
Other 2 5%
Student > Postgraduate 2 5%
Other 5 14%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 38%
Computer Science 8 22%
Biochemistry, Genetics and Molecular Biology 4 11%
Social Sciences 2 5%
Medicine and Dentistry 2 5%
Other 3 8%
Unknown 4 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 10 April 2018.
All research outputs
#3,002,509
of 12,781,938 outputs
Outputs from BioData Mining
#95
of 231 outputs
Outputs of similar age
#22,700
of 96,938 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 12,781,938 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 231 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.3. 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 96,938 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them