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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A p-Median approach for predicting drug response in tumour cells
|
|---|---|
| Published in |
BMC Bioinformatics, October 2014
|
| DOI | 10.1186/s12859-014-0353-7 |
| Pubmed ID | |
| Authors |
Elisabetta Fersini, Enza Messina, Francesco Archetti |
| Abstract |
The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 82% |
| India | 1 | 9% |
| Spain | 1 | 9% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 91% |
| Scientists | 1 | 9% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 5% |
| Netherlands | 1 | 2% |
| Unknown | 39 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 21% |
| Researcher | 9 | 21% |
| Other | 3 | 7% |
| Professor > Associate Professor | 3 | 7% |
| Student > Master | 3 | 7% |
| Other | 7 | 17% |
| Unknown | 8 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 26% |
| Biochemistry, Genetics and Molecular Biology | 6 | 14% |
| Agricultural and Biological Sciences | 4 | 10% |
| Chemistry | 4 | 10% |
| Mathematics | 2 | 5% |
| Other | 6 | 14% |
| Unknown | 9 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 03 December 2014.
All research outputs
#3,763,845
of 22,689,790 outputs
Outputs from BMC Bioinformatics
#1,454
of 7,252 outputs
Outputs of similar age
#44,557
of 260,588 outputs
Outputs of similar age from BMC Bioinformatics
#31
of 139 outputs
Altmetric has tracked 22,689,790 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,252 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 79% 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 260,588 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 82% of its contemporaries.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.