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Designing a post-genomics knowledge ecosystem to translate pharmacogenomics into public health action

Overview of attention for article published in Genome Medicine, November 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (78th percentile)

Mentioned by

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4 X users
wikipedia
2 Wikipedia pages

Citations

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21 Dimensions

Readers on

mendeley
96 Mendeley
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1 CiteULike
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Title
Designing a post-genomics knowledge ecosystem to translate pharmacogenomics into public health action
Published in
Genome Medicine, November 2012
DOI 10.1186/gm392
Pubmed ID
Authors

Edward S Dove, Samer A Faraj, Eugene Kolker, Vural Özdemir

Abstract

Translation of pharmacogenomics to public health action is at the epicenter of the life sciences agenda. Post-genomics knowledge is simultaneously co-produced at multiple scales and locales by scientists, crowd-sourcing and biological citizens. The latter are entrepreneurial citizens who are autonomous, self-governing and increasingly conceptualizing themselves in biological terms, ostensibly taking responsibility for their own health, and engaging in patient advocacy and health activism. By studying these heterogeneous 'scientific cultures', we can locate innovative parameters of collective action to move pharmacogenomics to practice (personalized therapeutics). To this end, we reconceptualize knowledge-based innovation as a complex ecosystem comprising 'actors' and 'narrators'. For robust knowledge translation, we require a nested post-genomics technology governance system composed of first-order narrators (for example, social scientists, philosophers, bioethicists) situated at arm's length from innovation actors (for example, pharmacogenomics scientists). Yet, second-order narrators (for example, an independent and possibly crowd-funded think-tank of citizen scholars, marginalized groups and knowledge end-users) are crucial to prevent first-order narrators from gaining excessive power that can be misused in the course of steering innovations. To operate such 'self-calibrating' and nested innovation ecosystems, we introduce the concept of 'wiki-governance' to enable mutual and iterative learning among innovation actors and first- and second-order narrators. '[A] scientific expert is someone who knows more and more about less and less, until finally knowing (almost) everything about (almost) nothing.' [1] 'Ubuntu: I am because you are.' [2].

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 96 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 1%
Germany 1 1%
Brazil 1 1%
United Kingdom 1 1%
Canada 1 1%
United States 1 1%
Unknown 90 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 21%
Student > Ph. D. Student 15 16%
Student > Master 15 16%
Student > Doctoral Student 8 8%
Student > Bachelor 7 7%
Other 15 16%
Unknown 16 17%
Readers by discipline Count As %
Medicine and Dentistry 16 17%
Social Sciences 11 11%
Business, Management and Accounting 10 10%
Agricultural and Biological Sciences 6 6%
Economics, Econometrics and Finance 6 6%
Other 26 27%
Unknown 21 22%
Attention Score in Context

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 29 August 2022.
All research outputs
#6,374,203
of 25,374,917 outputs
Outputs from Genome Medicine
#1,075
of 1,585 outputs
Outputs of similar age
#60,521
of 285,955 outputs
Outputs of similar age from Genome Medicine
#17
of 24 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.8. 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 285,955 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 24 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.