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Prediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression

Overview of attention for article published in BMC Systems Biology, June 2014
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1 tweeter

Citations

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16 Dimensions

Readers on

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33 Mendeley
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Title
Prediction of kinase inhibitor response using activity profiling, in vitro screening, and elastic net regression
Published in
BMC Systems Biology, June 2014
DOI 10.1186/1752-0509-8-74
Pubmed ID
Abstract

Many kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems. Predictions of drug effectiveness based on a comprehensive network model of cellular signalling are difficult, due to our partial knowledge of the complex biological processes downstream of the targeted kinases.

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

Geographical breakdown

Country Count As %
Finland 1 3%
United States 1 3%
Unknown 31 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 30%
Student > Ph. D. Student 9 27%
Student > Master 4 12%
Student > Doctoral Student 2 6%
Student > Bachelor 2 6%
Other 3 9%
Unknown 3 9%
Readers by discipline Count As %
Computer Science 7 21%
Agricultural and Biological Sciences 6 18%
Biochemistry, Genetics and Molecular Biology 6 18%
Chemistry 5 15%
Psychology 2 6%
Other 4 12%
Unknown 3 9%

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 21 July 2014.
All research outputs
#3,157,494
of 4,505,402 outputs
Outputs from BMC Systems Biology
#395
of 670 outputs
Outputs of similar age
#74,765
of 109,890 outputs
Outputs of similar age from BMC Systems Biology
#22
of 29 outputs
Altmetric has tracked 4,505,402 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 670 research outputs from this source. They receive a mean Attention Score of 2.7. This one is in the 27th percentile – i.e., 27% 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 109,890 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.