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Differential analysis of biological networks

Overview of attention for article published in BMC Bioinformatics, October 2015
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
70 Mendeley
citeulike
3 CiteULike
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Title
Differential analysis of biological networks
Published in
BMC Bioinformatics, October 2015
DOI 10.1186/s12859-015-0735-5
Pubmed ID
Authors

Da Ruan, Alastair Young, Giovanni Montana

Abstract

In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 4%
India 1 1%
Israel 1 1%
Germany 1 1%
Brazil 1 1%
United States 1 1%
Singapore 1 1%
Unknown 61 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 33%
Student > Ph. D. Student 23 33%
Student > Master 8 11%
Student > Postgraduate 4 6%
Professor 3 4%
Other 9 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 39%
Computer Science 16 23%
Biochemistry, Genetics and Molecular Biology 10 14%
Unspecified 7 10%
Mathematics 3 4%
Other 7 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 04 December 2015.
All research outputs
#5,812,157
of 11,293,566 outputs
Outputs from BMC Bioinformatics
#2,086
of 4,195 outputs
Outputs of similar age
#87,190
of 248,880 outputs
Outputs of similar age from BMC Bioinformatics
#66
of 147 outputs
Altmetric has tracked 11,293,566 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 4,195 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 47th percentile – i.e., 47% 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 248,880 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 64% of its contemporaries.
We're also able to compare this research output to 147 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 52% of its contemporaries.