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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A categorical network approach for discovering differentially expressed regulations in cancer
|
|---|---|
| Published in |
BMC Medical Genomics, November 2013
|
| DOI | 10.1186/1755-8794-6-s3-s1 |
| Pubmed ID | |
| Authors |
Nikolay Balov |
| Abstract |
The problem of efficient utilization of genome-wide expression profiles for identification and prediction of complex disease conditions is both important and challenging. Polygenic pathologies such as most types of cancer involve disregulation of many interacting genes which has prompted search for suitable statistical models for their representation. By accounting for changes in gene regulations between comparable conditions, graphical statistical models are expected to improve prediction precision. |
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Unknown | 16 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 35% |
| Researcher | 4 | 24% |
| Student > Doctoral Student | 3 | 18% |
| Student > Bachelor | 1 | 6% |
| Other | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 2 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 35% |
| Biochemistry, Genetics and Molecular Biology | 2 | 12% |
| Engineering | 2 | 12% |
| Agricultural and Biological Sciences | 2 | 12% |
| Veterinary Science and Veterinary Medicine | 1 | 6% |
| Other | 2 | 12% |
| Unknown | 2 | 12% |
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 21 October 2014.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from BMC Medical Genomics
#1,315
of 2,444 outputs
Outputs of similar age
#140,759
of 225,262 outputs
Outputs of similar age from BMC Medical Genomics
#19
of 36 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,444 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 36th percentile – i.e., 36% 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 225,262 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.