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.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes
|
|---|---|
| Published in |
BMC Bioinformatics, October 2013
|
| DOI | 10.1186/1471-2105-14-s16-s4 |
| Pubmed ID | |
| Authors |
Danai Laksameethanasan, Rui Zhen Tan, Geraldine Wei-Ling Toh, Lit-Hsin Loo |
| Abstract |
High-throughput, image-based screens of cellular responses to genetic or chemical perturbations generate huge numbers of cell images. Automated analysis is required to quantify and compare the effects of these perturbations. However, few of the current freely-available bioimage analysis software tools are optimized for efficient handling of these images. Even fewer of them are designed to transform the phenotypic features measured from these images into discriminative profiles that can reveal biologically meaningful associations among the tested perturbations. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 3% |
| Singapore | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 31 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 24% |
| Student > Bachelor | 6 | 18% |
| Professor > Associate Professor | 6 | 18% |
| Student > Ph. D. Student | 4 | 12% |
| Student > Master | 3 | 9% |
| Other | 3 | 9% |
| Unknown | 4 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 44% |
| Engineering | 6 | 18% |
| Computer Science | 4 | 12% |
| Medicine and Dentistry | 4 | 12% |
| Arts and Humanities | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 4 | 12% |
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 19 March 2015.
All research outputs
#18,806,562
of 23,306,612 outputs
Outputs from BMC Bioinformatics
#6,416
of 7,379 outputs
Outputs of similar age
#159,462
of 213,416 outputs
Outputs of similar age from BMC Bioinformatics
#94
of 115 outputs
Altmetric has tracked 23,306,612 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,379 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 213,416 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.