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X Demographics
Mendeley readers
Attention Score in Context
Title |
DLocalMotif: a discriminative approach for discovering local motifs in protein sequences
|
---|---|
Published in |
Bioinformatics, November 2012
|
DOI | 10.1093/bioinformatics/bts654 |
Pubmed ID | |
Authors |
Ahmed M. Mehdi, Muhammad Shoaib B. Sehgal, Bostjan Kobe, Timothy L. Bailey, Mikael Bodén |
Abstract |
Local motifs are patterns of DNA or protein sequences that occur within a sequence interval relative to a biologically defined anchor or landmark. Current protein motif discovery methods do not adequately consider such constraints to identify biologically significant motifs that are only weakly over-represented but spatially confined. Using negatives, i.e. sequences known to not contain a local motif, can further increase the specificity of their discovery. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 25% |
Peru | 1 | 25% |
United States | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Science communicators (journalists, bloggers, editors) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 29 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 28% |
Researcher | 7 | 24% |
Student > Master | 6 | 21% |
Professor > Associate Professor | 3 | 10% |
Professor | 1 | 3% |
Other | 3 | 10% |
Unknown | 1 | 3% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 15 | 52% |
Computer Science | 6 | 21% |
Biochemistry, Genetics and Molecular Biology | 4 | 14% |
Mathematics | 1 | 3% |
Engineering | 1 | 3% |
Other | 0 | 0% |
Unknown | 2 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 04 January 2013.
All research outputs
#14,915,133
of 25,374,917 outputs
Outputs from Bioinformatics
#8,880
of 12,809 outputs
Outputs of similar age
#111,043
of 196,745 outputs
Outputs of similar age from Bioinformatics
#83
of 148 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 40th percentile – i.e., 40% of other outputs scored the same or lower than it.
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 28th percentile – i.e., 28% 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 196,745 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 148 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.