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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Genome-wide in silico prediction of gene expression
|
|---|---|
| Published in |
Bioinformatics, September 2012
|
| DOI | 10.1093/bioinformatics/bts529 |
| Pubmed ID | |
| Authors |
Robert C. McLeay, Tom Lesluyes, Gabriel Cuellar Partida, Timothy L. Bailey |
| Abstract |
Modelling the regulation of gene expression can provide insight into the regulatory roles of individual transcription factors (TFs) and histone modifications. Recently, Ouyang et al. in 2009 modelled gene expression levels in mouse embryonic stem (mES) cells using in vivo ChIP-seq measurements of TF binding. ChIP-seq TF binding data, however, are tissue-specific and relatively difficult to obtain. This limits the applicability of gene expression models that rely on ChIP-seq TF binding data. |
X Demographics
The data shown below were collected from the profiles of 6 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 | 2 | 33% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Scientists | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| Portugal | 1 | <1% |
| Germany | 1 | <1% |
| France | 1 | <1% |
| Netherlands | 1 | <1% |
| Japan | 1 | <1% |
| Italy | 1 | <1% |
| Unknown | 120 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 39 | 30% |
| Student > Ph. D. Student | 36 | 28% |
| Student > Master | 15 | 12% |
| Professor > Associate Professor | 11 | 8% |
| Student > Bachelor | 4 | 3% |
| Other | 15 | 12% |
| Unknown | 10 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 70 | 54% |
| Biochemistry, Genetics and Molecular Biology | 23 | 18% |
| Computer Science | 15 | 12% |
| Chemistry | 3 | 2% |
| Mathematics | 2 | 2% |
| Other | 6 | 5% |
| Unknown | 11 | 8% |
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 24 October 2012.
All research outputs
#7,960,693
of 25,374,917 outputs
Outputs from Bioinformatics
#6,525
of 12,809 outputs
Outputs of similar age
#58,161
of 186,945 outputs
Outputs of similar age from Bioinformatics
#84
of 179 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 12,809 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one is in the 47th percentile – i.e., 47% 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 186,945 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 179 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.