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Predicting a small molecule-kinase interaction map: A machine learning approach

Overview of attention for article published in Journal of Cheminformatics, June 2011
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
36 Mendeley
citeulike
2 CiteULike
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Title
Predicting a small molecule-kinase interaction map: A machine learning approach
Published in
Journal of Cheminformatics, June 2011
DOI 10.1186/1758-2946-3-22
Pubmed ID
Authors

Fabian Buchwald, Lothar Richter, Stefan Kramer

Abstract

We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Russia 1 3%
Unknown 34 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 25%
Researcher 9 25%
Student > Master 5 14%
Other 4 11%
Student > Postgraduate 2 6%
Other 6 17%
Unknown 1 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 28%
Chemistry 9 25%
Computer Science 7 19%
Medicine and Dentistry 2 6%
Chemical Engineering 1 3%
Other 5 14%
Unknown 2 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 17 January 2012.
All research outputs
#1,786,778
of 4,505,992 outputs
Outputs from Journal of Cheminformatics
#144
of 253 outputs
Outputs of similar age
#1,414,335
of 3,441,619 outputs
Outputs of similar age from Journal of Cheminformatics
#144
of 253 outputs
Altmetric has tracked 4,505,992 research outputs across all sources so far. This one has received more attention than most of these and is in the 58th percentile.
So far Altmetric has tracked 253 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 40th percentile – i.e., 40% 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 3,441,619 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.
We're also able to compare this research output to 253 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.