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Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets

Overview of attention for article published in BMC Research Notes, November 2011
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Title
Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets
Published in
BMC Research Notes, November 2011
DOI 10.1186/1756-0500-4-504
Pubmed ID
Authors

Vinita Periwal, Jinuraj K Rajappan, Open Source Drug Discovery Consortium, Abdul UC Jaleel, Vinod Scaria

Abstract

Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 3 3%
Canada 2 2%
Germany 1 1%
Hungary 1 1%
Unknown 84 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 27%
Student > Master 13 14%
Researcher 12 13%
Student > Bachelor 10 11%
Student > Postgraduate 6 7%
Other 12 13%
Unknown 13 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 20%
Chemistry 15 16%
Computer Science 14 15%
Biochemistry, Genetics and Molecular Biology 9 10%
Medicine and Dentistry 7 8%
Other 13 14%
Unknown 15 16%
Attention Score in Context

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 23 November 2011.
All research outputs
#20,152,153
of 22,659,164 outputs
Outputs from BMC Research Notes
#3,542
of 4,246 outputs
Outputs of similar age
#216,841
of 238,400 outputs
Outputs of similar age from BMC Research Notes
#64
of 69 outputs
Altmetric has tracked 22,659,164 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,246 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 1st percentile – i.e., 1% 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 238,400 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 69 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.