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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Evidence-ranked motif identification
|
|---|---|
| Published in |
Genome Biology, February 2010
|
| DOI | 10.1186/gb-2010-11-2-r19 |
| Pubmed ID | |
| Authors |
Stoyan Georgiev, Alan P Boyle, Karthik Jayasurya, Xuan Ding, Sayan Mukherjee, Uwe Ohler |
| Abstract |
cERMIT is a computationally efficient motif discovery tool based on analyzing genome-wide quantitative regulatory evidence. Instead of pre-selecting promising candidate sequences, it utilizes information across all sequence regions to search for high-scoring motifs. We apply cERMIT on a range of direct binding and overexpression datasets; it substantially outperforms state-of-the-art approaches on curated ChIP-chip datasets, and easily scales to current mammalian ChIP-seq experiments with data on thousands of non-coding regions. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 4% |
| Germany | 3 | 2% |
| United Kingdom | 3 | 2% |
| France | 2 | 2% |
| Turkey | 1 | <1% |
| Australia | 1 | <1% |
| Sweden | 1 | <1% |
| South Africa | 1 | <1% |
| Israel | 1 | <1% |
| Other | 5 | 4% |
| Unknown | 101 | 81% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 37 | 30% |
| Student > Ph. D. Student | 31 | 25% |
| Student > Master | 19 | 15% |
| Professor > Associate Professor | 8 | 6% |
| Student > Bachelor | 7 | 6% |
| Other | 15 | 12% |
| Unknown | 7 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 68 | 55% |
| Biochemistry, Genetics and Molecular Biology | 24 | 19% |
| Computer Science | 10 | 8% |
| Mathematics | 3 | 2% |
| Medicine and Dentistry | 3 | 2% |
| Other | 5 | 4% |
| Unknown | 11 | 9% |
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 10 May 2020.
All research outputs
#7,959,162
of 25,371,288 outputs
Outputs from Genome Biology
#3,393
of 4,467 outputs
Outputs of similar age
#52,512
of 184,762 outputs
Outputs of similar age from Genome Biology
#17
of 24 outputs
Altmetric has tracked 25,371,288 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 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 23rd percentile – i.e., 23% 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 184,762 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 70% of its contemporaries.
We're also able to compare this research output to 24 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.