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Using systems biology to evaluate targets and mechanism of action of drugs for diabetes comorbidities

Overview of attention for article published in Diabetologia, July 2016
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Average Attention Score compared to outputs of the same age and source

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6 X users

Citations

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22 Mendeley
Title
Using systems biology to evaluate targets and mechanism of action of drugs for diabetes comorbidities
Published in
Diabetologia, July 2016
DOI 10.1007/s00125-016-4032-2
Pubmed ID
Authors

Bernd Mayer

Abstract

Medications approved for diabetes-associated renal and cardiovascular morbidities and candidate drugs currently in development are subject to substantial variability in drug response. Heterogeneity on a molecular phenotype level is not apparent at clinical presentation, which means that inter-individual differences in drug effect at the molecular level are masked. These findings identify the need for optimising patient phenotyping via use of molecular biomarkers for a personalised therapy approach. Molecular diversity may, on the one hand, result from the effect of genetic polymorphisms on drug transport, metabolism and effective target modulation. Equally relevant, differences may be due to molecular pathologies. The presence of distinct molecular phenotypes is suggested by classifiers aimed at modelling progressive disease. Such functions for prognosis incorporate a complex set of clinical variables or a multitude of molecular markers reflecting a diverse set of molecular disease mechanisms. This information on disease pathology and the mechanism of action of the drug needs to be systematically integrated with data on molecular biomarkers to develop an experimental tool for personalising medicine. The large amount of molecular data available for characterising diabetes-associated morbidities allows for elucidation of molecular process model representations of disease pathologies. Selecting biomarker candidates on such grounds and, in turn identifying their association with progressive disease allows for the identification of molecular processes associated with disease progression. The molecular effect of a drug can also be modelled at a molecular process level, and the integration of disease pathology and drug effect molecular models reveals candidate biomarkers for assessing drug response. Such tools serve as enrichment strategies aimed at adding precision to drug development and use.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 18%
Student > Ph. D. Student 3 14%
Other 2 9%
Student > Master 2 9%
Student > Bachelor 2 9%
Other 2 9%
Unknown 7 32%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 18%
Agricultural and Biological Sciences 4 18%
Medicine and Dentistry 2 9%
Computer Science 1 5%
Pharmacology, Toxicology and Pharmaceutical Science 1 5%
Other 2 9%
Unknown 8 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 27 January 2017.
All research outputs
#6,958,388
of 22,880,230 outputs
Outputs from Diabetologia
#2,708
of 5,038 outputs
Outputs of similar age
#115,003
of 354,139 outputs
Outputs of similar age from Diabetologia
#48
of 80 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 5,038 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.7. This one is in the 46th percentile – i.e., 46% 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 354,139 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.