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WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network

Overview of attention for article published in BMC Bioinformatics, November 2019
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Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
30 Mendeley
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Title
WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network
Published in
BMC Bioinformatics, November 2019
DOI 10.1186/s12859-019-3066-0
Authors

Yahui Long, Jiawei Luo

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

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 20%
Researcher 4 13%
Student > Bachelor 3 10%
Student > Master 3 10%
Student > Postgraduate 2 7%
Other 3 10%
Unknown 9 30%
Readers by discipline Count As %
Computer Science 7 23%
Biochemistry, Genetics and Molecular Biology 3 10%
Agricultural and Biological Sciences 3 10%
Unspecified 1 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 2 7%
Unknown 13 43%

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 03 November 2019.
All research outputs
#12,303,402
of 16,133,257 outputs
Outputs from BMC Bioinformatics
#4,614
of 5,846 outputs
Outputs of similar age
#224,563
of 330,213 outputs
Outputs of similar age from BMC Bioinformatics
#471
of 592 outputs
Altmetric has tracked 16,133,257 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,846 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 15th percentile – i.e., 15% 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 330,213 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 592 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.