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A computational framework for complex disease stratification from multiple large-scale datasets

Overview of attention for article published in BMC Systems Biology, May 2018
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (71st percentile)

Mentioned by

twitter
5 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
128 Mendeley
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Title
A computational framework for complex disease stratification from multiple large-scale datasets
Published in
BMC Systems Biology, May 2018
DOI 10.1186/s12918-018-0556-z
Pubmed ID
Authors

Bertrand De Meulder, Diane Lefaudeux, Aruna T. Bansal, Alexander Mazein, Amphun Chaiboonchoe, Hassan Ahmed, Irina Balaur, Mansoor Saqi, Johann Pellet, Stéphane Ballereau, Nathanaël Lemonnier, Kai Sun, Ioannis Pandis, Xian Yang, Manohara Batuwitage, Kosmas Kretsos, Jonathan van Eyll, Alun Bedding, Timothy Davison, Paul Dodson, Christopher Larminie, Anthony Postle, Julie Corfield, Ratko Djukanovic, Kian Fan Chung, Ian M. Adcock, Yi-Ke Guo, Peter J. Sterk, Alexander Manta, Anthony Rowe, Frédéric Baribaud, Charles Auffray

Abstract

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 128 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 128 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 33 26%
Student > Ph. D. Student 22 17%
Other 9 7%
Professor 8 6%
Student > Master 8 6%
Other 16 13%
Unknown 32 25%
Readers by discipline Count As %
Medicine and Dentistry 21 16%
Computer Science 18 14%
Biochemistry, Genetics and Molecular Biology 14 11%
Agricultural and Biological Sciences 9 7%
Pharmacology, Toxicology and Pharmaceutical Science 4 3%
Other 23 18%
Unknown 39 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 03 April 2021.
All research outputs
#4,875,779
of 20,638,493 outputs
Outputs from BMC Systems Biology
#164
of 1,137 outputs
Outputs of similar age
#84,246
of 297,907 outputs
Outputs of similar age from BMC Systems Biology
#1
of 1 outputs
Altmetric has tracked 20,638,493 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,137 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 85% 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 297,907 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 71% of its contemporaries.
We're also able to compare this research output to 1 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