↓ Skip to main content

A novel approach for biomarker selection and the integration of repeated measures experiments from two assays

Overview of attention for article published in BMC Bioinformatics, December 2012
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
87 Dimensions

Readers on

mendeley
179 Mendeley
citeulike
2 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
A novel approach for biomarker selection and the integration of repeated measures experiments from two assays
Published in
BMC Bioinformatics, December 2012
DOI 10.1186/1471-2105-13-325
Pubmed ID
Authors

Benoit Liquet, Kim-Anh Lê Cao, Hakim Hocini, Rodolphe Thiébaut

Abstract

High throughput 'omics' experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects.

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

Geographical breakdown

Country Count As %
France 2 1%
Estonia 2 1%
United States 2 1%
Spain 2 1%
South Africa 1 <1%
Switzerland 1 <1%
Ireland 1 <1%
Italy 1 <1%
Unknown 167 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 48 27%
Student > Ph. D. Student 45 25%
Student > Master 17 9%
Professor > Associate Professor 9 5%
Student > Postgraduate 8 4%
Other 26 15%
Unknown 26 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 43 24%
Biochemistry, Genetics and Molecular Biology 33 18%
Medicine and Dentistry 16 9%
Mathematics 7 4%
Computer Science 6 3%
Other 34 19%
Unknown 40 22%

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 06 December 2012.
All research outputs
#18,984,439
of 21,339,655 outputs
Outputs from BMC Bioinformatics
#6,515
of 6,922 outputs
Outputs of similar age
#257,127
of 291,323 outputs
Outputs of similar age from BMC Bioinformatics
#352
of 369 outputs
Altmetric has tracked 21,339,655 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,922 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 1st percentile – i.e., 1% 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 291,323 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 369 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.