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Mendeley readers
Attention Score in Context
| Title |
Motif Enrichment Analysis: a unified framework and an evaluation on ChIP data
|
|---|---|
| Published in |
BMC Bioinformatics, April 2010
|
| DOI | 10.1186/1471-2105-11-165 |
| Pubmed ID | |
| Authors |
Robert C McLeay, Timothy L Bailey |
| Abstract |
A major goal of molecular biology is determining the mechanisms that control the transcription of genes. Motif Enrichment Analysis (MEA) seeks to determine which DNA-binding transcription factors control the transcription of a set of genes by detecting enrichment of known binding motifs in the genes' regulatory regions. Typically, the biologist specifies a set of genes believed to be co-regulated and a library of known DNA-binding models for transcription factors, and MEA determines which (if any) of the factors may be direct regulators of the genes. Since the number of factors with known DNA-binding models is rapidly increasing as a result of high-throughput technologies, MEA is becoming increasingly useful. In this paper, we explore ways to make MEA applicable in more settings, and evaluate the efficacy of a number of MEA approaches. |
Mendeley readers
The data shown below were compiled from readership statistics for 475 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 1% |
| Germany | 4 | <1% |
| France | 2 | <1% |
| United Kingdom | 2 | <1% |
| Sweden | 1 | <1% |
| Japan | 1 | <1% |
| Mexico | 1 | <1% |
| Unknown | 459 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 131 | 28% |
| Researcher | 95 | 20% |
| Student > Bachelor | 43 | 9% |
| Student > Master | 42 | 9% |
| Student > Doctoral Student | 29 | 6% |
| Other | 57 | 12% |
| Unknown | 78 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 166 | 35% |
| Biochemistry, Genetics and Molecular Biology | 150 | 32% |
| Computer Science | 19 | 4% |
| Medicine and Dentistry | 12 | 3% |
| Engineering | 10 | 2% |
| Other | 32 | 7% |
| Unknown | 86 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 11 May 2020.
All research outputs
#3,598,731
of 22,649,029 outputs
Outputs from BMC Bioinformatics
#1,333
of 7,234 outputs
Outputs of similar age
#15,682
of 95,421 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 65 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,234 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 81% of its peers.
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 95,421 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 65 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.