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Attention Score in Context
Title |
Distance-based classifiers as potential diagnostic and prediction tools for human diseases
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Published in |
BMC Genomics, December 2014
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DOI | 10.1186/1471-2164-15-s12-s10 |
Pubmed ID | |
Authors |
Boris Veytsman, Lei Wang, Tiange Cui, Sergey Bruskin, Ancha Baranova |
Abstract |
Typically, gene expression biomarkers are being discovered in course of high-throughput experiments, for example, RNAseq or microarray profiling. Analytic pipelines that extract so-called signatures suffer from the "Dimensionality curse": the number of genes expressed exceeds the number of patients we can enroll in the study and use to train the discriminator algorithm. Hence, problems with the reproducibility of gene signatures are more common than not; when the algorithm is executed using a different training set, the resulting diagnostic signature may turn out to be completely different. |
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 % |
---|---|---|
Unknown | 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 25 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 36% |
Student > Bachelor | 5 | 20% |
Student > Master | 4 | 16% |
Student > Ph. D. Student | 2 | 8% |
Professor | 1 | 4% |
Other | 2 | 8% |
Unknown | 2 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 5 | 20% |
Computer Science | 5 | 20% |
Biochemistry, Genetics and Molecular Biology | 4 | 16% |
Agricultural and Biological Sciences | 2 | 8% |
Engineering | 2 | 8% |
Other | 2 | 8% |
Unknown | 5 | 20% |
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 27 August 2015.
All research outputs
#20,248,338
of 22,776,824 outputs
Outputs from BMC Genomics
#9,269
of 10,643 outputs
Outputs of similar age
#295,970
of 353,131 outputs
Outputs of similar age from BMC Genomics
#214
of 238 outputs
Altmetric has tracked 22,776,824 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 10,643 research outputs from this source. They receive a mean Attention Score of 4.7. 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 353,131 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 238 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.