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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A fast weak motif-finding algorithm based on community detection in graphs
|
|---|---|
| Published in |
BMC Bioinformatics, July 2013
|
| DOI | 10.1186/1471-2105-14-227 |
| Pubmed ID | |
| Authors |
Caiyan Jia, Matthew B Carson, Jian Yu |
| Abstract |
Identification of transcription factor binding sites (also called 'motif discovery') in DNA sequences is a basic step in understanding genetic regulation. Although many successful programs have been developed, the problem is far from being solved on account of diversity in gene expression/regulation and the low specificity of binding sites. State-of-the-art algorithms have their own constraints (e.g., high time or space complexity for finding long motifs, low precision in identification of weak motifs, or the OOPS constraint: one occurrence of the motif instance per sequence) which limit their scope of application. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Italy | 1 | 2% |
| Brazil | 1 | 2% |
| India | 1 | 2% |
| Iran, Islamic Republic of | 1 | 2% |
| Russia | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 53 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 29% |
| Researcher | 10 | 17% |
| Student > Master | 8 | 14% |
| Professor > Associate Professor | 5 | 8% |
| Other | 3 | 5% |
| Other | 8 | 14% |
| Unknown | 8 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 29% |
| Computer Science | 13 | 22% |
| Biochemistry, Genetics and Molecular Biology | 8 | 14% |
| Engineering | 4 | 7% |
| Business, Management and Accounting | 2 | 3% |
| Other | 5 | 8% |
| Unknown | 10 | 17% |
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 20 July 2013.
All research outputs
#14,172,390
of 22,714,025 outputs
Outputs from BMC Bioinformatics
#4,719
of 7,260 outputs
Outputs of similar age
#96,603
of 172,131 outputs
Outputs of similar age from BMC Bioinformatics
#62
of 95 outputs
Altmetric has tracked 22,714,025 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,260 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 30th percentile – i.e., 30% 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 172,131 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 95 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.