| Title |
Predictive modeling of anti-malarial molecules inhibiting apicoplast formation
|
|---|---|
| Published in |
BMC Bioinformatics, February 2013
|
| DOI | 10.1186/1471-2105-14-55 |
| Pubmed ID | |
| Authors |
Salma Jamal, Vinita Periwal, Open Source Drug Discovery Consortium, Vinod Scaria |
| Abstract |
Malaria is a major healthcare problem worldwide resulting in an estimated 0.65 million deaths every year. It is caused by the members of the parasite genus Plasmodium. The current therapeutic options for malaria are limited to a few classes of molecules, and are fast shrinking due to the emergence of widespread resistance to drugs in the pathogen. The recent availability of high-throughput phenotypic screen datasets for antimalarial activity offers a possibility to create computational models for bioactivity based on chemical descriptors of molecules with potential to accelerate drug discovery for malaria. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 33% |
| Norway | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 3% |
| United States | 1 | 1% |
| Ireland | 1 | 1% |
| Unknown | 65 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 20% |
| Researcher | 9 | 13% |
| Student > Master | 8 | 12% |
| Student > Postgraduate | 6 | 9% |
| Student > Doctoral Student | 4 | 6% |
| Other | 17 | 25% |
| Unknown | 11 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 17% |
| Computer Science | 10 | 14% |
| Medicine and Dentistry | 9 | 13% |
| Chemistry | 8 | 12% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Other | 12 | 17% |
| Unknown | 15 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 05 April 2016.
All research outputs
#5,622,528
of 22,696,971 outputs
Outputs from BMC Bioinformatics
#2,082
of 7,254 outputs
Outputs of similar age
#66,521
of 307,673 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 141 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,254 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 gotten more attention than average, scoring higher than 70% 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 307,673 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 78% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.