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X Demographics
Mendeley readers
Attention Score in Context
| Title |
HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening
|
|---|---|
| Published in |
BMC Bioinformatics, October 2013
|
| DOI | 10.1186/1471-2105-14-s16-s12 |
| Pubmed ID | |
| Authors |
Phasit Charoenkwan, Eric Hwang, Robert W Cutler, Hua-Chin Lee, Li-Wei Ko, Hui-Ling Huang, Shinn-Ying Ho |
| Abstract |
High-content screening (HCS) has become a powerful tool for drug discovery. However, the discovery of drugs targeting neurons is still hampered by the inability to accurately identify and quantify the phenotypic changes of multiple neurons in a single image (named multi-neuron image) of a high-content screen. Therefore, it is desirable to develop an automated image analysis method for analyzing multi-neuron images. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 25% |
| United Kingdom | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 50 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 11 | 22% |
| Student > Ph. D. Student | 9 | 18% |
| Student > Bachelor | 8 | 16% |
| Student > Master | 5 | 10% |
| Other | 4 | 8% |
| Other | 7 | 14% |
| Unknown | 6 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 19 | 38% |
| Computer Science | 8 | 16% |
| Biochemistry, Genetics and Molecular Biology | 4 | 8% |
| Neuroscience | 4 | 8% |
| Engineering | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 8 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 22 January 2015.
All research outputs
#13,394,135
of 22,727,570 outputs
Outputs from BMC Bioinformatics
#4,195
of 7,266 outputs
Outputs of similar age
#111,608
of 212,053 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 116 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,266 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 38th percentile – i.e., 38% 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 212,053 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 116 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.