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Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry

Overview of attention for article published in BMC Evolutionary Biology, May 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

Mentioned by

15 tweeters


9 Dimensions

Readers on

58 Mendeley
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Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry
Published in
BMC Evolutionary Biology, May 2015
DOI 10.1186/s12862-015-0352-y
Pubmed ID

Maria Anisimova


Today computational molecular evolution is a vibrant research field that benefits from the availability of large and complex new generation sequencing data - ranging from full genomes and proteomes to microbiomes, metabolomes and epigenomes. The grounds for this progress were established long before the discovery of the DNA structure. Specifically, Darwin's theory of evolution by means of natural selection not only remains relevant today, but also provides a solid basis for computational research with a variety of applications. But a long-term progress in biology was ensured by the mathematical sciences, as exemplified by Sir R. Fisher in early 20th century. Now this is true more than ever: The data size and its complexity require biologists to work in close collaboration with experts in computational sciences, modeling and statistics. Natural selection drives function conservation and adaptation to emerging pathogens or new environments; selection plays key role in immune and resistance systems. Here I focus on computational methods for evaluating selection in molecular sequences, and argue that they have a high potential for applications. Pharma and biotech industries can successfully use this potential, and should take the initiative to enhance their research and development with state of the art bioinformatics approaches. This review provides a quick guide to the current computational approaches that apply the evolutionary principles of natural selection to real life problems - from drug target validation, vaccine design and protein engineering to applications in agriculture, ecology and conservation.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 2%
Germany 1 2%
Netherlands 1 2%
Brazil 1 2%
Finland 1 2%
Spain 1 2%
United States 1 2%
Philippines 1 2%
Poland 1 2%
Other 0 0%
Unknown 49 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 22%
Researcher 13 22%
Student > Bachelor 9 16%
Professor 6 10%
Student > Master 5 9%
Other 8 14%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 43%
Biochemistry, Genetics and Molecular Biology 14 24%
Computer Science 4 7%
Environmental Science 3 5%
Medicine and Dentistry 2 3%
Other 6 10%
Unknown 4 7%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 12 February 2021.
All research outputs
of 20,271,528 outputs
Outputs from BMC Evolutionary Biology
of 2,861 outputs
Outputs of similar age
of 241,450 outputs
Outputs of similar age from BMC Evolutionary Biology
of 1 outputs
Altmetric has tracked 20,271,528 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,861 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has gotten more attention than average, scoring higher than 73% of its peers.
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 241,450 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 82% of its contemporaries.
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