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X Demographics
Mendeley readers
Attention Score in Context
| Title |
MoTeX-II: structured MoTif eXtraction from large-scale datasets
|
|---|---|
| Published in |
BMC Bioinformatics, July 2014
|
| DOI | 10.1186/1471-2105-15-235 |
| Pubmed ID | |
| Authors |
Solon P Pissis |
| Abstract |
Identifying repeated factors that occur in a string of letters or common factors that occur in a set of strings represents an important task in computer science and biology. Such patterns are called motifs, and the process of identifying them is called motif extraction. In biology, motif extraction constitutes a fundamental step in understanding regulation of gene expression. State-of-the-art tools for motif extraction have their own constraints. Most of these tools are only designed for single motif extraction; structured motifs additionally allow for distance intervals between their single motif components. Moreover, motif extraction from large-scale datasets-for instance, large-scale ChIP-Seq datasets-cannot be performed by current tools. Other constraints include high time and/or space complexity for identifying long motifs with higher error thresholds. |
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 % |
|---|---|---|
| Norway | 1 | 33% |
| Mexico | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Members of the public | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Norway | 1 | 6% |
| Unknown | 15 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 53% |
| Student > Bachelor | 2 | 12% |
| Student > Master | 2 | 12% |
| Professor | 1 | 6% |
| Other | 1 | 6% |
| Other | 2 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 47% |
| Agricultural and Biological Sciences | 6 | 35% |
| Biochemistry, Genetics and Molecular Biology | 2 | 12% |
| Medicine and Dentistry | 1 | 6% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 November 2014.
All research outputs
#3,111,761
of 22,758,248 outputs
Outputs from BMC Bioinformatics
#1,134
of 7,272 outputs
Outputs of similar age
#32,017
of 225,827 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 138 outputs
Altmetric has tracked 22,758,248 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,272 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 84% 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 225,827 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 85% of its contemporaries.
We're also able to compare this research output to 138 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.