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Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations

Overview of attention for article published in BMC Bioinformatics, October 2011
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1 tweeter

Citations

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15 Dimensions

Readers on

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25 Mendeley
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1 CiteULike
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Title
Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations
Published in
BMC Bioinformatics, October 2011
DOI 10.1186/1471-2105-12-386
Pubmed ID
Authors

Koen Van der Borght, Elke Van Craenenbroeck, Pierre Lecocq, Margriet Van Houtte, Barbara Van Kerckhove, Lee Bacheler, Geert Verbeke, Herman van Vlijmen

Abstract

Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.

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

Geographical breakdown

Country Count As %
Brazil 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 24%
Researcher 4 16%
Student > Doctoral Student 2 8%
Other 2 8%
Professor > Associate Professor 2 8%
Other 5 20%
Unknown 4 16%
Readers by discipline Count As %
Medicine and Dentistry 6 24%
Chemistry 3 12%
Agricultural and Biological Sciences 3 12%
Pharmacology, Toxicology and Pharmaceutical Science 2 8%
Nursing and Health Professions 1 4%
Other 6 24%
Unknown 4 16%

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 25 October 2011.
All research outputs
#15,237,301
of 22,655,397 outputs
Outputs from BMC Bioinformatics
#5,353
of 7,236 outputs
Outputs of similar age
#91,857
of 132,705 outputs
Outputs of similar age from BMC Bioinformatics
#65
of 83 outputs
Altmetric has tracked 22,655,397 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 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 18th percentile – i.e., 18% 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 132,705 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.