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A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms

Overview of attention for article published in BMC Bioinformatics, June 2014
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Mentioned by

twitter
3 X users

Readers on

mendeley
48 Mendeley
citeulike
1 CiteULike
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Title
A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH® terms
Published in
BMC Bioinformatics, June 2014
DOI 10.1186/1471-2105-15-179
Pubmed ID
Authors

Toshihide Ono, Satoru Kuhara

Abstract

Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 4%
France 1 2%
Netherlands 1 2%
Sweden 1 2%
United Kingdom 1 2%
Unknown 42 88%

Demographic breakdown

Readers by professional status Count As %
Student > Master 11 23%
Researcher 9 19%
Student > Ph. D. Student 9 19%
Other 5 10%
Student > Bachelor 5 10%
Other 4 8%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 27%
Computer Science 10 21%
Biochemistry, Genetics and Molecular Biology 6 13%
Medicine and Dentistry 4 8%
Engineering 3 6%
Other 7 15%
Unknown 5 10%
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 09 April 2021.
All research outputs
#15,301,754
of 22,757,090 outputs
Outputs from BMC Bioinformatics
#5,372
of 7,272 outputs
Outputs of similar age
#133,710
of 229,145 outputs
Outputs of similar age from BMC Bioinformatics
#101
of 158 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 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 18th percentile – i.e., 18% 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 229,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.