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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational genetics analysis of grey matter density in Alzheimer’s disease
|
|---|---|
| Published in |
BioData Mining, August 2014
|
| DOI | 10.1186/1756-0381-7-17 |
| Pubmed ID | |
| Authors |
Amanda L Zieselman, Jonathan M Fisher, Ting Hu, Peter C Andrews, Casey S Greene, Li Shen, Andrew J Saykin, Jason H Moore |
| Abstract |
Alzheimer's disease is the most common form of progressive dementia and there is currently no known cure. The cause of onset is not fully understood but genetic factors are expected to play a significant role. We present here a bioinformatics approach to the genetic analysis of grey matter density as an endophenotype for late onset Alzheimer's disease. Our approach combines machine learning analysis of gene-gene interactions with large-scale functional genomics data for assessing biological relationships. |
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 % |
|---|---|---|
| United States | 5 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 34 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 26% |
| Student > Master | 7 | 20% |
| Student > Postgraduate | 5 | 14% |
| Researcher | 4 | 11% |
| Student > Bachelor | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 6 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 17% |
| Agricultural and Biological Sciences | 6 | 17% |
| Computer Science | 4 | 11% |
| Neuroscience | 4 | 11% |
| Medicine and Dentistry | 2 | 6% |
| Other | 5 | 14% |
| Unknown | 8 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 27 August 2014.
All research outputs
#7,933,918
of 24,162,141 outputs
Outputs from BioData Mining
#163
of 317 outputs
Outputs of similar age
#76,779
of 239,860 outputs
Outputs of similar age from BioData Mining
#8
of 8 outputs
Altmetric has tracked 24,162,141 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 317 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 48th percentile – i.e., 48% 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 239,860 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.