↓ Skip to main content

Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug

Overview of attention for article published in BMC Systems Biology, July 2012
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
74 Dimensions

Readers on

mendeley
115 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug
Published in
BMC Systems Biology, July 2012
DOI 10.1186/1752-0509-6-80
Pubmed ID
Authors

Hee Sook Lee, Taejeong Bae, Ji-Hyun Lee, Dae Gyu Kim, Young Sun Oh, Yeongjun Jang, Ji-Tea Kim, Jong-Jun Lee, Alessio Innocenti, Claudiu T Supuran, Luonan Chen, Kyoohyoung Rho, Sunghoon Kim

Abstract

The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning. In this study, we have established a database we call "PharmDB" which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death. By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
France 1 <1%
Netherlands 1 <1%
United Kingdom 1 <1%
Brazil 1 <1%
Unknown 107 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 24%
Researcher 23 20%
Student > Master 18 16%
Student > Doctoral Student 11 10%
Other 10 9%
Other 22 19%
Unknown 3 3%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 23%
Computer Science 23 20%
Medicine and Dentistry 18 16%
Biochemistry, Genetics and Molecular Biology 11 10%
Pharmacology, Toxicology and Pharmaceutical Science 7 6%
Other 20 17%
Unknown 9 8%

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 July 2012.
All research outputs
#5,485,574
of 9,722,866 outputs
Outputs from BMC Systems Biology
#541
of 949 outputs
Outputs of similar age
#53,886
of 101,432 outputs
Outputs of similar age from BMC Systems Biology
#29
of 46 outputs
Altmetric has tracked 9,722,866 research outputs across all sources so far. This one is in the 26th percentile – i.e., 26% of other outputs scored the same or lower than it.
So far Altmetric has tracked 949 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 31st percentile – i.e., 31% 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 101,432 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 46 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.