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Machine learning identifies a compact gene set for monitoring the circadian clock in human blood

Overview of attention for article published in Genome Medicine, February 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#40 of 1,044)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

news
6 news outlets
blogs
1 blog
twitter
79 tweeters
facebook
1 Facebook page

Readers on

mendeley
61 Mendeley
citeulike
1 CiteULike
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Title
Machine learning identifies a compact gene set for monitoring the circadian clock in human blood
Published in
Genome Medicine, February 2017
DOI 10.1186/s13073-017-0406-4
Pubmed ID
Authors

Jacob J. Hughey

Abstract

The circadian clock and the daily rhythms it produces are crucial for human health, but are often disrupted by the modern environment. At the same time, circadian rhythms may influence the efficacy and toxicity of therapeutics and the metabolic response to food intake. Developing treatments for circadian dysfunction, as well as optimizing the daily timing of treatments for other health conditions, will require a simple and accurate method to monitor the molecular state of the circadian clock. Here we used a recently developed method called ZeitZeiger to predict circadian time (CT, time of day according to the circadian clock) from genome-wide gene expression in human blood. In cross-validation on 498 samples from 60 individuals across three publicly available datasets, ZeitZeiger predicted CT in single samples with a median absolute error of 2.1 h. The predictor trained on all 498 samples used 15 genes, only two of which are part of the core circadian clock. By then applying ZeitZeiger to 475 additional samples from the same three datasets, we quantified how the circadian clock in the blood was affected by various perturbations to the sleep-wake and light-dark cycles. Finally, we extended ZeitZeiger (1) to handle intra-individual variation by making predictions based on multiple samples taken a known time apart, and (2) to handle inter-individual variation by personalizing predictions based on samples from the respective individual. Each of these strategies improved prediction of CT by ~20%. Our results are an important step towards precision circadian medicine. In addition, our generalizable extensions to ZeitZeiger may be applicable to the growing number of biological datasets that contain multiple observations per individual.

Twitter Demographics

The data shown below were collected from the profiles of 79 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 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 3%
Iran, Islamic Republic of 1 2%
Unknown 58 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 28%
Researcher 10 16%
Student > Bachelor 9 15%
Student > Master 5 8%
Other 3 5%
Other 6 10%
Unknown 11 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 20%
Biochemistry, Genetics and Molecular Biology 10 16%
Medicine and Dentistry 9 15%
Computer Science 7 11%
Neuroscience 3 5%
Other 7 11%
Unknown 13 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 94. 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 02 October 2017.
All research outputs
#214,237
of 15,265,759 outputs
Outputs from Genome Medicine
#40
of 1,044 outputs
Outputs of similar age
#7,754
of 260,394 outputs
Outputs of similar age from Genome Medicine
#1
of 6 outputs
Altmetric has tracked 15,265,759 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,044 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.7. This one has done particularly well, scoring higher than 96% 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 260,394 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 6 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