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Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network

Overview of attention for article published in BMC Bioinformatics, December 2019
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2 X users

Citations

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35 Dimensions

Readers on

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46 Mendeley
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Title
Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network
Published in
BMC Bioinformatics, December 2019
DOI 10.1186/s12859-019-3288-1
Pubmed ID
Authors

Hui Liu, Wenhao Zhang, Lixia Nie, Xiancheng Ding, Judong Luo, Ling Zou

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 24%
Student > Master 6 13%
Researcher 6 13%
Student > Bachelor 2 4%
Other 1 2%
Other 1 2%
Unknown 19 41%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 17%
Computer Science 5 11%
Agricultural and Biological Sciences 4 9%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Engineering 2 4%
Other 5 11%
Unknown 20 43%
Attention Score in Context

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 11 December 2019.
All research outputs
#18,703,173
of 23,179,757 outputs
Outputs from BMC Bioinformatics
#6,379
of 7,344 outputs
Outputs of similar age
#336,832
of 459,125 outputs
Outputs of similar age from BMC Bioinformatics
#183
of 231 outputs
Altmetric has tracked 23,179,757 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,344 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 5th percentile – i.e., 5% 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 459,125 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 231 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.