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Mendeley readers
Attention Score in Context
| Title |
Exploration of multivariate analysis in microbial coding sequence modeling
|
|---|---|
| Published in |
BMC Bioinformatics, May 2012
|
| DOI | 10.1186/1471-2105-13-97 |
| Pubmed ID | |
| Authors |
Tahir Mehmood, Jon Bohlin, Anja Bråthen Kristoffersen, Solve Sæbø, Jonas Warringer, Lars Snipen |
| Abstract |
Gene finding is a complicated procedure that encapsulates algorithms for coding sequence modeling, identification of promoter regions, issues concerning overlapping genes and more. In the present study we focus on coding sequence modeling algorithms; that is, algorithms for identification and prediction of the actual coding sequences from genomic DNA. In this respect, we promote a novel multivariate method known as Canonical Powered Partial Least Squares (CPPLS) as an alternative to the commonly used Interpolated Markov model (IMM). Comparisons between the methods were performed on DNA, codon and protein sequences with highly conserved genes taken from several species with different genomic properties. |
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 % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
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 % |
|---|---|---|
| Mexico | 1 | 6% |
| United States | 1 | 6% |
| Norway | 1 | 6% |
| Unknown | 14 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 47% |
| Professor > Associate Professor | 2 | 12% |
| Professor | 2 | 12% |
| Student > Ph. D. Student | 2 | 12% |
| Other | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 53% |
| Computer Science | 2 | 12% |
| Engineering | 2 | 12% |
| Medicine and Dentistry | 1 | 6% |
| Economics, Econometrics and Finance | 1 | 6% |
| Other | 0 | 0% |
| Unknown | 2 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 15 May 2012.
All research outputs
#20,157,329
of 22,665,794 outputs
Outputs from BMC Bioinformatics
#6,819
of 7,247 outputs
Outputs of similar age
#148,521
of 163,891 outputs
Outputs of similar age from BMC Bioinformatics
#98
of 103 outputs
Altmetric has tracked 22,665,794 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 1st percentile – i.e., 1% 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 163,891 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.