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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 Ecology and Evolution, May 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

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Title
Darwin and Fisher meet at biotech: on the potential of computational molecular evolution in industry
Published in
BMC Ecology and Evolution, May 2015
DOI 10.1186/s12862-015-0352-y
Pubmed ID
Authors

Maria Anisimova

Abstract

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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 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 59 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 50 85%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 22%
Researcher 12 20%
Student > Bachelor 9 15%
Student > Master 7 12%
Professor 6 10%
Other 8 14%
Unknown 4 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 23 39%
Biochemistry, Genetics and Molecular Biology 15 25%
Computer Science 4 7%
Environmental Science 3 5%
Social Sciences 3 5%
Other 7 12%
Unknown 4 7%
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 18 May 2015.
All research outputs
#6,275,904
of 25,373,627 outputs
Outputs from BMC Ecology and Evolution
#1,352
of 3,714 outputs
Outputs of similar age
#68,471
of 278,920 outputs
Outputs of similar age from BMC Ecology and Evolution
#29
of 67 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,714 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.5. This one has gotten more attention than average, scoring higher than 63% 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 278,920 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 75% of its contemporaries.
We're also able to compare this research output to 67 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.