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Stochastic epigenetic outliers can define field defects in cancer

Overview of attention for article published in BMC Bioinformatics, April 2016
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Title
Stochastic epigenetic outliers can define field defects in cancer
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1056-z
Pubmed ID
Authors

Andrew E. Teschendorff, Allison Jones, Martin Widschwendter

Abstract

There is growing evidence that DNA methylation alterations may contribute to carcinogenesis. Recent data also suggest that DNA methylation field defects in normal pre-neoplastic tissue represent infrequent stochastic "outlier" events. This presents a statistical challenge for standard feature selection algorithms, which assume frequent alterations in a disease phenotype. Although differential variability has emerged as a novel feature selection paradigm for the discovery of outliers, a growing concern is that these could result from technical confounders, in principle thus favouring algorithms which are robust to outliers. Here we evaluate five differential variability algorithms in over 700 DNA methylomes, including two of the largest cohorts profiling precursor cancer lesions, and demonstrate that most of the novel proposed algorithms lack the sensitivity to detect epigenetic field defects at genome-wide significance. In contrast, algorithms which recognise heterogeneous outlier DNA methylation patterns are able to identify many sites in pre-neoplastic lesions, which display progression in invasive cancer. Thus, we show that many DNA methylation outliers are not technical artefacts, but define epigenetic field defects which are selected for during cancer progression. Given that cancer studies aiming to find epigenetic field defects are likely to be limited by sample size, adopting the novel feature selection paradigm advocated here will be critical to increase assay sensitivity.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 33%
Researcher 5 11%
Professor 5 11%
Student > Master 5 11%
Other 3 7%
Other 7 15%
Unknown 6 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 30%
Agricultural and Biological Sciences 12 26%
Computer Science 3 7%
Mathematics 2 4%
Psychology 2 4%
Other 5 11%
Unknown 8 17%
Attention Score in Context

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 17 October 2017.
All research outputs
#14,847,187
of 22,865,319 outputs
Outputs from BMC Bioinformatics
#5,053
of 7,295 outputs
Outputs of similar age
#170,016
of 298,997 outputs
Outputs of similar age from BMC Bioinformatics
#78
of 106 outputs
Altmetric has tracked 22,865,319 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,295 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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 298,997 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.