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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Assessment of genome annotation using gene function similarity within the gene neighborhood
|
|---|---|
| Published in |
BMC Bioinformatics, July 2017
|
| DOI | 10.1186/s12859-017-1761-2 |
| Pubmed ID | |
| Authors |
Se-Ran Jun, Intawat Nookaew, Loren Hauser, Andrey Gorin |
| Abstract |
Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments. We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria. Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations. |
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 % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 42 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 7 | 17% |
| Researcher | 6 | 14% |
| Student > Master | 6 | 14% |
| Student > Ph. D. Student | 4 | 10% |
| Student > Postgraduate | 3 | 7% |
| Other | 10 | 24% |
| Unknown | 6 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 24% |
| Biochemistry, Genetics and Molecular Biology | 9 | 21% |
| Immunology and Microbiology | 3 | 7% |
| Medicine and Dentistry | 3 | 7% |
| Engineering | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 12 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 25 January 2018.
All research outputs
#6,482,317
of 22,990,068 outputs
Outputs from BMC Bioinformatics
#2,491
of 7,309 outputs
Outputs of similar age
#103,924
of 315,216 outputs
Outputs of similar age from BMC Bioinformatics
#28
of 96 outputs
Altmetric has tracked 22,990,068 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,309 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 gotten more attention than average, scoring higher than 64% 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 315,216 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.