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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Computational model for compressed sensing RNAi cellular screening
|
|---|---|
| Published in |
BMC Bioinformatics, December 2012
|
| DOI | 10.1186/1471-2105-13-337 |
| Pubmed ID | |
| Authors |
Hua Tan, Jing Fan, Jiguang Bao, Jennifer G Dy, Xiaobo Zhou |
| Abstract |
RNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 4% |
| Belgium | 1 | 4% |
| Unknown | 22 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 29% |
| Student > Ph. D. Student | 3 | 13% |
| Student > Doctoral Student | 2 | 8% |
| Other | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 4 | 17% |
| Unknown | 4 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 33% |
| Computer Science | 3 | 13% |
| Medicine and Dentistry | 3 | 13% |
| Mathematics | 2 | 8% |
| Physics and Astronomy | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 5 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 04 January 2013.
All research outputs
#2,427,186
of 22,691,736 outputs
Outputs from BMC Bioinformatics
#761
of 7,255 outputs
Outputs of similar age
#25,692
of 280,466 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 127 outputs
Altmetric has tracked 22,691,736 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 7,255 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 done well, scoring higher than 89% 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 280,466 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 127 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.