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Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19

Overview of attention for article published in BMC Genetics, February 2016
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
3 tweeters

Citations

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28 Dimensions

Readers on

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81 Mendeley
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Title
Machine learning and data mining in complex genomic data—a review on the lessons learned in Genetic Analysis Workshop 19
Published in
BMC Genetics, February 2016
DOI 10.1186/s12863-015-0315-8
Pubmed ID
Authors

Inke R. König, Jonathan Auerbach, Damian Gola, Elizabeth Held, Emily R. Holzinger, Marc-André Legault, Rui Sun, Nathan Tintle, Hsin-Chou Yang

Abstract

In the analysis of current genomic data, application of machine learning and data mining techniques has become more attractive given the rising complexity of the projects. As part of the Genetic Analysis Workshop 19, approaches from this domain were explored, mostly motivated from two starting points. First, assuming an underlying structure in the genomic data, data mining might identify this and thus improve downstream association analyses. Second, computational methods for machine learning need to be developed further to efficiently deal with the current wealth of data.In the course of discussing results and experiences from the machine learning and data mining approaches, six common messages were extracted. These depict the current state of these approaches in the application to complex genomic data. Although some challenges remain for future studies, important forward steps were taken in the integration of different data types and the evaluation of the evidence. Mining the data for underlying genetic or phenotypic structure and using this information in subsequent analyses proved to be extremely helpful and is likely to become of even greater use with more complex data sets.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 81 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 1%
Bulgaria 1 1%
Denmark 1 1%
Belgium 1 1%
Unknown 77 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 23%
Researcher 12 15%
Student > Master 9 11%
Student > Bachelor 7 9%
Student > Doctoral Student 4 5%
Other 12 15%
Unknown 18 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 22%
Computer Science 16 20%
Biochemistry, Genetics and Molecular Biology 9 11%
Medicine and Dentistry 5 6%
Engineering 5 6%
Other 9 11%
Unknown 19 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 13 April 2016.
All research outputs
#5,291,311
of 10,258,653 outputs
Outputs from BMC Genetics
#312
of 770 outputs
Outputs of similar age
#146,111
of 343,145 outputs
Outputs of similar age from BMC Genetics
#11
of 45 outputs
Altmetric has tracked 10,258,653 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 770 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 57% 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 343,145 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 55% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.