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Machine learning derived risk prediction of anorexia nervosa

Overview of attention for article published in BMC Medical Genomics, January 2016
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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 (80th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
10 tweeters

Citations

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9 Dimensions

Readers on

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68 Mendeley
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Title
Machine learning derived risk prediction of anorexia nervosa
Published in
BMC Medical Genomics, January 2016
DOI 10.1186/s12920-016-0165-x
Pubmed ID
Authors

Yiran Guo, Zhi Wei, Brendan J. Keating, Hakon Hakonarson

Abstract

Anorexia nervosa (AN) is a complex psychiatric disease with a moderate to strong genetic contribution. In addition to conventional genome wide association (GWA) studies, researchers have been using machine learning methods in conjunction with genomic data to predict risk of diseases in which genetics play an important role. In this study, we collected whole genome genotyping data on 3940 AN cases and 9266 controls from the Genetic Consortium for Anorexia Nervosa (GCAN), the Wellcome Trust Case Control Consortium 3 (WTCCC3), Price Foundation Collaborative Group and the Children's Hospital of Philadelphia (CHOP), and applied machine learning methods for predicting AN disease risk. The prediction performance is measured by area under the receiver operating characteristic curve (AUC), indicating how well the model distinguishes cases from unaffected control subjects. Logistic regression model with the lasso penalty technique generated an AUC of 0.693, while Support Vector Machines and Gradient Boosted Trees reached AUC's of 0.691 and 0.623, respectively. Using different sample sizes, our results suggest that larger datasets are required to optimize the machine learning models and achieve higher AUC values. To our knowledge, this is the first attempt to assess AN risk based on genome wide genotype level data. Future integration of genomic, environmental and family-based information is likely to improve the AN risk evaluation process, eventually benefitting AN patients and families in the clinical setting.

Twitter Demographics

The data shown below were collected from the profiles of 10 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 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 22%
Student > Master 12 18%
Researcher 11 16%
Student > Bachelor 6 9%
Professor 4 6%
Other 12 18%
Unknown 8 12%
Readers by discipline Count As %
Medicine and Dentistry 10 15%
Biochemistry, Genetics and Molecular Biology 7 10%
Computer Science 7 10%
Neuroscience 6 9%
Agricultural and Biological Sciences 5 7%
Other 18 26%
Unknown 15 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 16 February 2016.
All research outputs
#1,042,870
of 7,200,541 outputs
Outputs from BMC Medical Genomics
#65
of 400 outputs
Outputs of similar age
#60,880
of 318,123 outputs
Outputs of similar age from BMC Medical Genomics
#4
of 16 outputs
Altmetric has tracked 7,200,541 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 400 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done well, scoring higher than 83% 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 318,123 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 80% of its contemporaries.
We're also able to compare this research output to 16 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.