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Network medicine: linking disorders

Overview of attention for article published in Human Genetics, July 2012
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5 CiteULike
Title
Network medicine: linking disorders
Published in
Human Genetics, July 2012
DOI 10.1007/s00439-012-1206-y
Pubmed ID
Authors

Rosario M. Piro

Abstract

The molecular events underlying many human hereditary disorders remain to be discovered despite the significant advances made in molecular biology and genetics in the past years. Given the complexity of cellular systems and the interplay between different functional modules, it is becoming increasingly evident that profound insights into human disease cannot be derived by analyzing single genetic defects. The generation of different types of disease interaction networks has recently emerged as a unifying approach that holds the promise of shedding some light on common pathological mechanisms by placing the single disorders into a larger context. In this review, I summarize the rationale behind these disease networks and different ways of constructing them. Finally, I highlight some of the first results that have been obtained by systematically analyzing the intertwined relationships between human disorders because they suggest that the current disease classification does not always sufficiently reflect biologically and medically relevant disease relationships.

X Demographics

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

Geographical breakdown

Country Count As %
France 3 4%
United Kingdom 2 2%
Germany 1 1%
Brazil 1 1%
Italy 1 1%
Spain 1 1%
United States 1 1%
Unknown 73 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 23%
Student > Ph. D. Student 17 20%
Student > Bachelor 6 7%
Other 6 7%
Professor 5 6%
Other 12 14%
Unknown 18 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 30%
Biochemistry, Genetics and Molecular Biology 9 11%
Computer Science 7 8%
Medicine and Dentistry 5 6%
Mathematics 3 4%
Other 10 12%
Unknown 24 29%
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 24 August 2012.
All research outputs
#18,313,878
of 22,675,759 outputs
Outputs from Human Genetics
#2,691
of 2,949 outputs
Outputs of similar age
#126,084
of 164,603 outputs
Outputs of similar age from Human Genetics
#15
of 21 outputs
Altmetric has tracked 22,675,759 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 2,949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.2. This one is in the 4th percentile – i.e., 4% 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 164,603 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.