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Attention Score in Context
| Chapter title |
Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms
|
|---|---|
| Chapter number | 5006 |
| Book title |
Protein and Sugar Export and Assembly in Gram-positive Bacteria
|
| Published in |
Current topics in microbiology and immunology, January 2016
|
| DOI | 10.1007/82_2015_5006 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-956012-0, 978-3-31-956014-4
|
| Authors |
Henrik Nielsen, Nielsen, Henrik |
| Abstract |
When predicting the subcellular localization of proteins from their amino acid sequences, there are basically three approaches: signal-based, global property-based, and homology-based. Each of these has its advantages and drawbacks, and it is important when comparing methods to know which approach was used. Various statistical and machine learning algorithms are used with all three approaches, and various measures and standards are employed when reporting the performances of the developed methods. This chapter presents a number of available methods for prediction of sorting signals and subcellular localization, but rather than providing a checklist of which predictors to use, it aims to function as a guide for critical assessment of prediction methods. |
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 % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 5% |
| Unknown | 21 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 23% |
| Student > Postgraduate | 4 | 18% |
| Student > Ph. D. Student | 4 | 18% |
| Student > Bachelor | 2 | 9% |
| Researcher | 2 | 9% |
| Other | 2 | 9% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 36% |
| Agricultural and Biological Sciences | 5 | 23% |
| Computer Science | 3 | 14% |
| Immunology and Microbiology | 1 | 5% |
| Engineering | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 4 | 18% |
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 11 January 2016.
All research outputs
#18,434,182
of 22,837,982 outputs
Outputs from Current topics in microbiology and immunology
#525
of 679 outputs
Outputs of similar age
#285,378
of 394,936 outputs
Outputs of similar age from Current topics in microbiology and immunology
#33
of 55 outputs
Altmetric has tracked 22,837,982 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 679 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one is in the 11th percentile – i.e., 11% 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 394,936 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 55 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.