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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A new computational strategy for predicting essential genes
|
|---|---|
| Published in |
BMC Genomics, December 2013
|
| DOI | 10.1186/1471-2164-14-910 |
| Pubmed ID | |
| Authors |
Jian Cheng, Wenwu Wu, Yinwen Zhang, Xiangchen Li, Xiaoqian Jiang, Gehong Wei, Shiheng Tao |
| Abstract |
Determination of the minimum gene set for cellular life is one of the central goals in biology. Genome-wide essential gene identification has progressed rapidly in certain bacterial species; however, it remains difficult to achieve in most eukaryotic species. Several computational models have recently been developed to integrate gene features and used as alternatives to transfer gene essentiality annotations between organisms. |
X Demographics
The data shown below were collected from the profiles of 24 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 | 6 | 25% |
| Mexico | 2 | 8% |
| Italy | 1 | 4% |
| United Kingdom | 1 | 4% |
| Canada | 1 | 4% |
| Poland | 1 | 4% |
| Israel | 1 | 4% |
| Germany | 1 | 4% |
| France | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 8 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 19 | 79% |
| Members of the public | 5 | 21% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 2 | 3% |
| India | 2 | 3% |
| United Kingdom | 1 | 1% |
| Chile | 1 | 1% |
| Unknown | 69 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 32% |
| Researcher | 12 | 16% |
| Student > Master | 8 | 11% |
| Student > Bachelor | 6 | 8% |
| Student > Postgraduate | 3 | 4% |
| Other | 8 | 11% |
| Unknown | 14 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 33 | 44% |
| Biochemistry, Genetics and Molecular Biology | 10 | 13% |
| Computer Science | 10 | 13% |
| Medicine and Dentistry | 2 | 3% |
| Environmental Science | 1 | 1% |
| Other | 2 | 3% |
| Unknown | 17 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 31 January 2014.
All research outputs
#2,687,538
of 25,736,439 outputs
Outputs from BMC Genomics
#756
of 11,316 outputs
Outputs of similar age
#29,722
of 322,412 outputs
Outputs of similar age from BMC Genomics
#17
of 210 outputs
Altmetric has tracked 25,736,439 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,316 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 93% 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 322,412 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.