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Automated selection of synthetic biology parts for genetic regulatory networks.

Overview of attention for article published in ACS Synthetic Biology, July 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

blogs
1 blog
twitter
5 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Readers on

mendeley
74 Mendeley
citeulike
2 CiteULike
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Title
Automated selection of synthetic biology parts for genetic regulatory networks.
Published in
ACS Synthetic Biology, July 2012
DOI 10.1021/sb300032y
Pubmed ID
Authors

Fusun Yaman, Swapnil Bhatia, Aaron Adler, Douglas Densmore, Jacob Beal

Abstract

Raising the level of abstraction for synthetic biology design requires solving several challenging problems, including mapping abstract designs to DNA sequences. In this paper we present the first formalism and algorithms to address this problem. The key steps of this transformation are feature matching, signal matching, and part matching. Feature matching ensures that the mapping satisfies the regulatory relationships in the abstract design. Signal matching ensures that the expression levels of functional units are compatible. Finally, part matching finds a DNA part sequence that can implement the design. Our software tool MatchMaker implements these three steps.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Belgium 3 4%
United Kingdom 2 3%
India 1 1%
France 1 1%
Brazil 1 1%
Austria 1 1%
Unknown 61 82%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 38%
Researcher 18 24%
Student > Master 10 14%
Student > Bachelor 6 8%
Professor > Associate Professor 4 5%
Other 8 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 43%
Engineering 11 15%
Biochemistry, Genetics and Molecular Biology 9 12%
Computer Science 9 12%
Unspecified 6 8%
Other 7 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 10 October 2014.
All research outputs
#687,457
of 8,009,718 outputs
Outputs from ACS Synthetic Biology
#152
of 754 outputs
Outputs of similar age
#8,115
of 94,269 outputs
Outputs of similar age from ACS Synthetic Biology
#6
of 23 outputs
Altmetric has tracked 8,009,718 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 754 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.0. This one has done well, scoring higher than 79% 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 94,269 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 91% of its contemporaries.
We're also able to compare this research output to 23 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 73% of its contemporaries.