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Modules, networks and systems medicine for understanding disease and aiding diagnosis

Overview of attention for article published in Genome Medicine, October 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Good Attention Score compared to outputs of the same age and source (74th percentile)

Mentioned by

twitter
21 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
122 Dimensions

Readers on

mendeley
247 Mendeley
citeulike
4 CiteULike
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Title
Modules, networks and systems medicine for understanding disease and aiding diagnosis
Published in
Genome Medicine, October 2014
DOI 10.1186/s13073-014-0082-6
Pubmed ID
Authors

Mika Gustafsson, Colm E Nestor, Huan Zhang, Albert-László Barabási, Sergio Baranzini, Sören Brunak, Kian Fan Chung, Howard J Federoff, Anne-Claude Gavin, Richard R Meehan, Paola Picotti, Miguel Ángel Pujana, Nikolaus Rajewsky, Kenneth GC Smith, Peter J Sterk, Pablo Villoslada, Mikael Benson

Abstract

Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.

Twitter Demographics

The data shown below were collected from the profiles of 21 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 247 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Denmark 2 <1%
United Kingdom 2 <1%
Korea, Republic of 2 <1%
India 1 <1%
Italy 1 <1%
Brazil 1 <1%
Sweden 1 <1%
France 1 <1%
Other 8 3%
Unknown 226 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 62 25%
Student > Ph. D. Student 46 19%
Student > Master 39 16%
Other 24 10%
Student > Bachelor 15 6%
Other 40 16%
Unknown 21 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 55 22%
Biochemistry, Genetics and Molecular Biology 47 19%
Medicine and Dentistry 36 15%
Computer Science 35 14%
Neuroscience 7 3%
Other 32 13%
Unknown 35 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 14. 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 12 March 2015.
All research outputs
#1,361,028
of 15,466,176 outputs
Outputs from Genome Medicine
#332
of 1,055 outputs
Outputs of similar age
#23,554
of 233,254 outputs
Outputs of similar age from Genome Medicine
#18
of 70 outputs
Altmetric has tracked 15,466,176 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,055 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.6. This one has gotten more attention than average, scoring higher than 68% of its peers.
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 233,254 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.