You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
Attention Score in Context
| Title |
Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
|
|---|---|
| Published in |
BMC Bioinformatics, September 2007
|
| DOI | 10.1186/1471-2105-8-340 |
| Pubmed ID | |
| Authors |
Andrew A Hill, Peter LaPan, Yizheng Li, Steve Haney |
| Abstract |
High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells. |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 4% |
| Germany | 2 | 4% |
| Netherlands | 1 | 2% |
| Spain | 1 | 2% |
| Unknown | 50 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 18 | 32% |
| Researcher | 14 | 25% |
| Student > Doctoral Student | 7 | 13% |
| Student > Master | 6 | 11% |
| Student > Postgraduate | 2 | 4% |
| Other | 4 | 7% |
| Unknown | 5 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 30% |
| Computer Science | 14 | 25% |
| Medicine and Dentistry | 7 | 13% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Mathematics | 2 | 4% |
| Other | 7 | 13% |
| Unknown | 6 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 24 September 2018.
All research outputs
#2,918,443
of 22,660,862 outputs
Outputs from BMC Bioinformatics
#1,046
of 7,241 outputs
Outputs of similar age
#7,145
of 69,816 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 43 outputs
Altmetric has tracked 22,660,862 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,241 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 85% 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 69,816 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.