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Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation

Overview of attention for article published in BMC Medical Genomics, November 2013
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1 tweeter

Citations

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Title
Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation
Published in
BMC Medical Genomics, November 2013
DOI 10.1186/1755-8794-6-s3-s4
Pubmed ID
Authors

Yu-Fen Huang, Hsiang-Yuan Yeh, Von-Wun Soo

Abstract

During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.

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

Geographical breakdown

Country Count As %
United States 1 2%
Unknown 48 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 31%
Student > Master 8 16%
Student > Postgraduate 5 10%
Researcher 4 8%
Student > Bachelor 3 6%
Other 6 12%
Unknown 8 16%
Readers by discipline Count As %
Computer Science 14 29%
Biochemistry, Genetics and Molecular Biology 8 16%
Medicine and Dentistry 6 12%
Agricultural and Biological Sciences 5 10%
Engineering 4 8%
Other 3 6%
Unknown 9 18%

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 October 2014.
All research outputs
#16,628,262
of 18,796,975 outputs
Outputs from BMC Medical Genomics
#835
of 997 outputs
Outputs of similar age
#200,997
of 241,991 outputs
Outputs of similar age from BMC Medical Genomics
#36
of 39 outputs
Altmetric has tracked 18,796,975 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 997 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 1st percentile – i.e., 1% 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 241,991 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.