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Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

Overview of attention for article published in BMC Systems Biology, October 2011
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1 X user

Citations

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Title
Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network
Published in
BMC Systems Biology, October 2011
DOI 10.1186/1752-0509-5-158
Pubmed ID
Authors

Hui Liu, Jianzhong Su, Junhua Li, Hongbo Liu, Jie Lv, Boyan Li, Hong Qiao, Yan Zhang

Abstract

As an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI) network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 6%
Netherlands 1 2%
Germany 1 2%
Belgium 1 2%
Unknown 45 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 10 20%
Student > Master 7 14%
Professor > Associate Professor 4 8%
Student > Bachelor 3 6%
Other 7 14%
Unknown 7 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 31%
Computer Science 11 22%
Biochemistry, Genetics and Molecular Biology 6 12%
Medicine and Dentistry 4 8%
Nursing and Health Professions 2 4%
Other 5 10%
Unknown 7 14%
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 October 2011.
All research outputs
#18,297,449
of 22,653,392 outputs
Outputs from BMC Systems Biology
#835
of 1,142 outputs
Outputs of similar age
#112,982
of 135,951 outputs
Outputs of similar age from BMC Systems Biology
#33
of 41 outputs
Altmetric has tracked 22,653,392 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 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 11th percentile – i.e., 11% 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 135,951 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 41 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.