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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana
|
|---|---|
| Published in |
BMC Bioinformatics, November 2013
|
| DOI | 10.1186/1471-2105-14-329 |
| Pubmed ID | |
| Authors |
Salma Jamal, Vinod Scaria |
| Abstract |
Leishmaniasis is a neglected tropical disease which affects approx. 12 million individuals worldwide and caused by parasite Leishmania. The current drugs used in the treatment of Leishmaniasis are highly toxic and has seen widespread emergence of drug resistant strains which necessitates the need for the development of new therapeutic options. The high throughput screen data available has made it possible to generate computational predictive models which have the ability to assess the active scaffolds in a chemical library followed by its ADME/toxicity properties in the biological trials. |
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 % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 2% |
| United States | 1 | <1% |
| Brazil | 1 | <1% |
| Unknown | 107 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 14% |
| Student > Bachelor | 15 | 14% |
| Student > Master | 14 | 13% |
| Student > Ph. D. Student | 13 | 12% |
| Student > Doctoral Student | 6 | 5% |
| Other | 20 | 18% |
| Unknown | 27 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 14 | 13% |
| Agricultural and Biological Sciences | 13 | 12% |
| Medicine and Dentistry | 9 | 8% |
| Engineering | 7 | 6% |
| Computer Science | 7 | 6% |
| Other | 25 | 23% |
| Unknown | 36 | 32% |
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 16 December 2013.
All research outputs
#17,703,558
of 22,731,677 outputs
Outputs from BMC Bioinformatics
#5,924
of 7,266 outputs
Outputs of similar age
#219,527
of 302,097 outputs
Outputs of similar age from BMC Bioinformatics
#71
of 103 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% 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 13th percentile – i.e., 13% 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 302,097 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 103 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.