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BioBanking as the central tool for translational medicine CTM issue 2013

Overview of attention for article published in Clinical and Translational Medicine, February 2013
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Mentioned by

1 tweeter
2 Facebook pages


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Readers on

28 Mendeley
BioBanking as the central tool for translational medicine CTM issue 2013
Published in
Clinical and Translational Medicine, February 2013
DOI 10.1186/2001-1326-2-4
Pubmed ID

György Marko‐Varga


THE IMPACT OF MAPPING THE HUMAN PROTEOME: Globally, the health care organizations are under resource and cost constrains due to the increasing number of patients that are due to a fast increase of the 65+ age group requiring extensive medical hospitalization and treatments. Hospitals worldwide strive to seek the best cure for patients, suffering from various diseases. A consequence of these global changes, of healthy populations in relation to patients forms the basis for the build of large and centralized biobank facilities, with strategies where the search for an understanding of diseases at a molecular level is at heart. The efforts made lies within large governmental resource allocations where patient centers are collecting samples from clinical study participants in order to try to discover universal expression patterns and molecular signatures of disease and disease stages. Most developments in this area are aimed towards the discovery, and understanding diagnosis implementations. By providing the right treatment alternatives for patients care, at the right time point i.e., at a given disease stage development becomes a major goal where pharmaceutical industry, academia and the health care sector joins forces in large clinical epidemiological, population-, and disease based studies. This becomes a clear strategic link to the enhancement and prospects for personalized medicines and target directed diagnosis developments (Companion Diagnostics), which require coordinated efforts across a wide range of disciplines. Currently, companion diagnostics is at the core of the personalized medicine paradigm shift. It will identify patients who are most likely to benefit from a particular therapeutic product, as well as identify patients likely to be at increased risk for serious adverse reactions as a result of treatment with a particular therapeutic agent. It is predicted that more than half of all new drugs will require a companion diagnostic, which opens up for an endeavor for Proteomics research implementations.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 4%
Netherlands 1 4%
United States 1 4%
China 1 4%
Unknown 24 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 32%
Student > Ph. D. Student 3 11%
Student > Postgraduate 3 11%
Other 2 7%
Student > Master 2 7%
Other 4 14%
Unknown 5 18%
Readers by discipline Count As %
Medicine and Dentistry 9 32%
Agricultural and Biological Sciences 5 18%
Biochemistry, Genetics and Molecular Biology 3 11%
Pharmacology, Toxicology and Pharmaceutical Science 2 7%
Arts and Humanities 1 4%
Other 2 7%
Unknown 6 21%

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 09 May 2013.
All research outputs
of 15,414,857 outputs
Outputs from Clinical and Translational Medicine
of 243 outputs
Outputs of similar age
of 251,137 outputs
Outputs of similar age from Clinical and Translational Medicine
of 1 outputs
Altmetric has tracked 15,414,857 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 243 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 34th percentile – i.e., 34% 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 251,137 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them