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A note on utilising binary features as ligand descriptors

Overview of attention for article published in Journal of Cheminformatics, December 2015
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1 tweeter

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15 Mendeley
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3 CiteULike
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Title
A note on utilising binary features as ligand descriptors
Published in
Journal of Cheminformatics, December 2015
DOI 10.1186/s13321-015-0105-3
Pubmed ID
Authors

Hamse Y. Mussa, John B. O. Mitchell, Robert C. Glen

Abstract

It is common in cheminformatics to represent the properties of a ligand as a string of 1's and 0's, with the intention of elucidating, inter alia, the relationship between the chemical structure of a ligand and its bioactivity. In this commentary we note that, where relevant but non-redundant features are binary, they inevitably lead to a classifier capable of capturing only a linear relationship between structural features and activity. If, instead, we were to use relevant but non-redundant real-valued features, the resulting predictive model would be capable of describing a non-linear structure-activity relationship. Hence, we suggest that real-valued features, where available, are to be preferred in this scenario.

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

Geographical breakdown

Country Count As %
Germany 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 40%
Student > Master 4 27%
Student > Bachelor 2 13%
Student > Ph. D. Student 1 7%
Lecturer > Senior Lecturer 1 7%
Other 1 7%
Readers by discipline Count As %
Chemistry 7 47%
Computer Science 3 20%
Agricultural and Biological Sciences 2 13%
Biochemistry, Genetics and Molecular Biology 1 7%
Unknown 2 13%

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 20 January 2016.
All research outputs
#3,460,412
of 6,997,887 outputs
Outputs from Journal of Cheminformatics
#265
of 335 outputs
Outputs of similar age
#163,767
of 315,482 outputs
Outputs of similar age from Journal of Cheminformatics
#15
of 19 outputs
Altmetric has tracked 6,997,887 research outputs across all sources so far. This one is in the 28th percentile – i.e., 28% of other outputs scored the same or lower than it.
So far Altmetric has tracked 335 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 14th percentile – i.e., 14% 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 315,482 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 19 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.