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Distance-based classifiers as potential diagnostic and prediction tools for human diseases

Overview of attention for article published in BMC Genomics, December 2014
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Title
Distance-based classifiers as potential diagnostic and prediction tools for human diseases
Published in
BMC Genomics, December 2014
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.

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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.
Mendeley readers

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

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
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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.
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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.