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Attention Score in Context
| Title |
A phenotypic screening platform to identify small molecule modulators of Chlamydomonas reinhardtiigrowth, motility and photosynthesis
|
|---|---|
| Published in |
Genome Biology, November 2012
|
| DOI | 10.1186/gb-2012-13-11-r105 |
| Pubmed ID | |
| Authors |
Simon E Alfred, Anuradha Surendra, Chris Le, Ken Lin, Alexander Mok, Iain M Wallace, Michael Proctor, Malene L Urbanus, Guri Giaever, Corey Nislow |
| Abstract |
ABSTRACT: Chemical biology, the interfacial discipline of using small molecules as probes to investigate biology, is a powerful approach of developing specific, rapidly acting tools that can be applied across organisms. The single-celled alga Chlamydomonas reinhardtii is an excellent model system because of its photosynthetic ability, cilia-related motility and simple genetics. We report the results of an automated fitness screen of 5,445 small molecules and subsequent assays on motility/phototaxis and photosynthesis. Cheminformatic analysis revealed active core structures and was used to construct a naïve Bayes model that successfully predicts algal bioactive compounds. |
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 Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 1% |
| Unknown | 71 | 99% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 31% |
| Researcher | 15 | 21% |
| Student > Bachelor | 12 | 17% |
| Student > Master | 10 | 14% |
| Professor | 3 | 4% |
| Other | 5 | 7% |
| Unknown | 5 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 39 | 54% |
| Biochemistry, Genetics and Molecular Biology | 10 | 14% |
| Computer Science | 3 | 4% |
| Engineering | 2 | 3% |
| Chemistry | 2 | 3% |
| Other | 7 | 10% |
| Unknown | 9 | 13% |
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 26 November 2012.
All research outputs
#22,758,309
of 25,373,627 outputs
Outputs from Genome Biology
#4,394
of 4,467 outputs
Outputs of similar age
#255,453
of 285,521 outputs
Outputs of similar age from Genome Biology
#43
of 45 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 1st percentile – i.e., 1% 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 285,521 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.