You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
The LeFE algorithm: embracing the complexity of gene expression in the interpretation of microarray data
|
|---|---|
| Published in |
Genome Biology, September 2007
|
| DOI | 10.1186/gb-2007-8-9-r187 |
| Pubmed ID | |
| Authors |
Gabriel S Eichler, Mark Reimers, David Kane, John N Weinstein |
| Abstract |
Interpretation of microarray data remains a challenge, and most methods fail to consider the complex, nonlinear regulation of gene expression. To address that limitation, we introduce Learner of Functional Enrichment (LeFE), a statistical/machine learning algorithm based on Random Forest, and demonstrate it on several diverse datasets: smoker/never smoker, breast cancer classification, and cancer drug sensitivity. We also compare it with previously published algorithms, including Gene Set Enrichment Analysis. LeFE regularly identifies statistically significant functional themes consistent with known biology. |
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 % |
|---|---|---|
| India | 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 6% |
| Israel | 1 | 2% |
| Netherlands | 1 | 2% |
| Sweden | 1 | 2% |
| Unknown | 55 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 29% |
| Student > Ph. D. Student | 10 | 16% |
| Professor > Associate Professor | 9 | 15% |
| Professor | 6 | 10% |
| Student > Master | 4 | 6% |
| Other | 6 | 10% |
| Unknown | 9 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 24 | 39% |
| Medicine and Dentistry | 9 | 15% |
| Computer Science | 7 | 11% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Mathematics | 2 | 3% |
| Other | 7 | 11% |
| Unknown | 10 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 18 December 2013.
All research outputs
#16,048,009
of 25,374,917 outputs
Outputs from Genome Biology
#4,001
of 4,467 outputs
Outputs of similar age
#70,570
of 82,438 outputs
Outputs of similar age from Genome Biology
#34
of 40 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 9th percentile – i.e., 9% 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 82,438 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 40 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.