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Attention Score in Context
Chapter title |
A Computational Network Biology Approach to Uncover Novel Genes Related to Alzheimer's Disease.
|
---|---|
Chapter number | 26 |
Book title |
Systems Biology of Alzheimer's Disease
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Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-2627-5_26 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2626-8, 978-1-4939-2627-5
|
Authors |
Zanzoni, Andreas, Andreas Zanzoni |
Editors |
Juan I. Castrillo, Stephen G. Oliver |
Abstract |
Recent advances in the fields of genetics and genomics have enabled the identification of numerous Alzheimer's disease (AD) candidate genes, although for many of them the role in AD pathophysiology has not been uncovered yet. Concomitantly, network biology studies have shown a strong link between protein network connectivity and disease. In this chapter I describe a computational approach that, by combining local and global network analysis strategies, allows the formulation of novel hypotheses on the molecular mechanisms involved in AD and prioritizes candidate genes for further functional studies. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 2 | 40% |
United States | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 7% |
Unknown | 14 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 53% |
Student > Bachelor | 2 | 13% |
Student > Ph. D. Student | 1 | 7% |
Unknown | 4 | 27% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 3 | 20% |
Environmental Science | 1 | 7% |
Biochemistry, Genetics and Molecular Biology | 1 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 7% |
Computer Science | 1 | 7% |
Other | 4 | 27% |
Unknown | 4 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 2015.
All research outputs
#2,497,433
of 23,203,401 outputs
Outputs from Methods in molecular biology
#457
of 13,301 outputs
Outputs of similar age
#45,007
of 395,391 outputs
Outputs of similar age from Methods in molecular biology
#67
of 1,471 outputs
Altmetric has tracked 23,203,401 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,301 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 96% 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 395,391 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 88% of its contemporaries.
We're also able to compare this research output to 1,471 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.