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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, January 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)

Mentioned by

4 tweeters
2 Wikipedia pages


20 Dimensions

Readers on

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

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


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].

Twitter Demographics

The data shown below were collected from the profiles of 4 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 93 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 87 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 22%
Student > Master 15 16%
Student > Ph. D. Student 14 15%
Student > Doctoral Student 8 9%
Student > Bachelor 7 8%
Other 15 16%
Unknown 14 15%
Readers by discipline Count As %
Medicine and Dentistry 16 17%
Social Sciences 11 12%
Business, Management and Accounting 9 10%
Engineering 7 8%
Agricultural and Biological Sciences 6 6%
Other 25 27%
Unknown 19 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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
of 22,130,231 outputs
Outputs from Genome Medicine
of 1,402 outputs
Outputs of similar age
of 184,562 outputs
Outputs of similar age from Genome Medicine
of 4 outputs
Altmetric has tracked 22,130,231 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,402 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.5. This one is in the 30th percentile – i.e., 30% 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 184,562 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 74% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.