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In silico comparative characterization of pharmacogenomic missense variants

Overview of attention for article published in BMC Genomics, January 2014
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Title
In silico comparative characterization of pharmacogenomic missense variants
Published in
BMC Genomics, January 2014
DOI 10.1186/1471-2164-15-s4-s4
Pubmed ID
Authors

Biao Li, Chet Seligman, Janita Thusberg, Jackson L Miller, Jim Auer, Michelle Whirl-Carrillo, Emidio Capriotti, Teri E Klein, Sean D Mooney

Abstract

Missense pharmacogenomic (PGx) variants refer to amino acid substitutions that potentially affect the pharmacokinetic (PK) or pharmacodynamic (PD) response to drug therapies. The PGx variants, as compared to disease-associated variants, have not been investigated as deeply. The ability to computationally predict future PGx variants is desirable; however, it is not clear what data sets should be used or what features are beneficial to this end. Hence we carried out a comparative characterization of PGx variants with annotated neutral and disease variants from UniProt, to test the predictive power of sequence conservation and structural information in discriminating these three groups.

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

Geographical breakdown

Country Count As %
Sri Lanka 1 6%
France 1 6%
Unknown 14 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 25%
Researcher 3 19%
Student > Bachelor 2 13%
Lecturer 2 13%
Professor 2 13%
Other 3 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 38%
Biochemistry, Genetics and Molecular Biology 5 31%
Medicine and Dentistry 3 19%
Pharmacology, Toxicology and Pharmaceutical Science 1 6%
Chemistry 1 6%
Other 0 0%

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 13 January 2015.
All research outputs
#2,666,318
of 5,036,026 outputs
Outputs from BMC Genomics
#2,958
of 4,591 outputs
Outputs of similar age
#91,784
of 178,465 outputs
Outputs of similar age from BMC Genomics
#189
of 302 outputs
Altmetric has tracked 5,036,026 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,591 research outputs from this source. They receive a mean Attention Score of 3.8. This one is in the 25th percentile – i.e., 25% 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 178,465 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 302 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.