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Mendeley readers
Attention Score in Context
| Title |
Engineering proteinase K using machine learning and synthetic genes
|
|---|---|
| Published in |
BMC Biotechnology, March 2007
|
| DOI | 10.1186/1472-6750-7-16 |
| Pubmed ID | |
| Authors |
Jun Liao, Manfred K Warmuth, Sridhar Govindarajan, Jon E Ness, Rebecca P Wang, Claes Gustafsson, Jeremy Minshull |
| Abstract |
Altering a protein's function by changing its sequence allows natural proteins to be converted into useful molecular tools. Current protein engineering methods are limited by a lack of high throughput physical or computational tests that can accurately predict protein activity under conditions relevant to its final application. Here we describe a new synthetic biology approach to protein engineering that avoids these limitations by combining high throughput gene synthesis with machine learning-based design algorithms. |
Mendeley readers
The data shown below were compiled from readership statistics for 219 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 4% |
| United Kingdom | 5 | 2% |
| France | 1 | <1% |
| Germany | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 203 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 53 | 24% |
| Researcher | 50 | 23% |
| Student > Bachelor | 24 | 11% |
| Other | 17 | 8% |
| Student > Master | 11 | 5% |
| Other | 29 | 13% |
| Unknown | 35 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 65 | 30% |
| Biochemistry, Genetics and Molecular Biology | 51 | 23% |
| Chemistry | 16 | 7% |
| Computer Science | 12 | 5% |
| Engineering | 11 | 5% |
| Other | 20 | 9% |
| Unknown | 44 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 05 October 2022.
All research outputs
#4,877,901
of 23,476,369 outputs
Outputs from BMC Biotechnology
#263
of 946 outputs
Outputs of similar age
#14,940
of 77,874 outputs
Outputs of similar age from BMC Biotechnology
#2
of 4 outputs
Altmetric has tracked 23,476,369 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 946 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has gotten more attention than average, scoring higher than 65% 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 77,874 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 71% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.