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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Non-canonical peroxisome targeting signals: identification of novel PTS1 tripeptides and characterization of enhancer elements by computational permutation analysis
|
|---|---|
| Published in |
BMC Plant Biology, August 2012
|
| DOI | 10.1186/1471-2229-12-142 |
| Pubmed ID | |
| Authors |
Gopal Chowdhary, Amr RA Kataya, Thomas Lingner, Sigrun Reumann |
| Abstract |
High-accuracy prediction tools are essential in the post-genomic era to define organellar proteomes in their full complexity. We recently applied a discriminative machine learning approach to predict plant proteins carrying peroxisome targeting signals (PTS) type 1 from genome sequences. For Arabidopsis thaliana 392 gene models were predicted to be peroxisome-targeted. The predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 2% |
| Germany | 1 | 2% |
| Czechia | 1 | 2% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 56 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 9 | 15% |
| Student > Ph. D. Student | 8 | 13% |
| Researcher | 7 | 11% |
| Student > Master | 7 | 11% |
| Unspecified | 3 | 5% |
| Other | 13 | 21% |
| Unknown | 14 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 33% |
| Biochemistry, Genetics and Molecular Biology | 15 | 25% |
| Computer Science | 4 | 7% |
| Materials Science | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Other | 4 | 7% |
| Unknown | 14 | 23% |
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 February 2013.
All research outputs
#18,329,207
of 22,696,971 outputs
Outputs from BMC Plant Biology
#2,067
of 3,211 outputs
Outputs of similar age
#128,625
of 167,511 outputs
Outputs of similar age from BMC Plant Biology
#17
of 24 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,211 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 22nd percentile – i.e., 22% 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 167,511 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.
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 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.