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Attention Score in Context
Title |
A novel approach for biomarker selection and the integration of repeated measures experiments from two assays
|
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Published in |
BMC Bioinformatics, December 2012
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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. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Norway | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 191 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 | 179 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 49 | 26% |
Student > Ph. D. Student | 45 | 24% |
Student > Master | 17 | 9% |
Professor > Associate Professor | 11 | 6% |
Other | 9 | 5% |
Other | 29 | 15% |
Unknown | 31 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 45 | 24% |
Biochemistry, Genetics and Molecular Biology | 35 | 18% |
Medicine and Dentistry | 17 | 9% |
Mathematics | 7 | 4% |
Computer Science | 6 | 3% |
Other | 34 | 18% |
Unknown | 47 | 25% |
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
#20,176,348
of 22,689,790 outputs
Outputs from BMC Bioinformatics
#6,825
of 7,252 outputs
Outputs of similar age
#246,509
of 278,002 outputs
Outputs of similar age from BMC Bioinformatics
#121
of 127 outputs
Altmetric has tracked 22,689,790 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 7,252 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 278,002 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 127 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.