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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Core module biomarker identification with network exploration for breast cancer metastasis
|
|---|---|
| Published in |
BMC Bioinformatics, January 2012
|
| DOI | 10.1186/1471-2105-13-12 |
| Pubmed ID | |
| Authors |
Ruoting Yang, Bernie J Daigle, Linda R Petzold, Francis J Doyle |
| Abstract |
In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module". |
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 % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 2% |
| Mexico | 1 | 2% |
| Hungary | 1 | 2% |
| Germany | 1 | 2% |
| Unknown | 43 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 45% |
| Researcher | 8 | 17% |
| Professor | 3 | 6% |
| Student > Master | 3 | 6% |
| Student > Bachelor | 2 | 4% |
| Other | 7 | 15% |
| Unknown | 3 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 30% |
| Computer Science | 11 | 23% |
| Engineering | 7 | 15% |
| Biochemistry, Genetics and Molecular Biology | 4 | 9% |
| Mathematics | 2 | 4% |
| Other | 4 | 9% |
| Unknown | 5 | 11% |
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 25 January 2012.
All research outputs
#18,304,230
of 22,662,201 outputs
Outputs from BMC Bioinformatics
#6,278
of 7,241 outputs
Outputs of similar age
#196,218
of 245,904 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 71 outputs
Altmetric has tracked 22,662,201 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 7,241 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 245,904 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 71 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.