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A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions

Overview of attention for article published in BMC Systems Biology, October 2013
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1 tweeter

Citations

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50 Dimensions

Readers on

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133 Mendeley
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2 CiteULike
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Title
A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions
Published in
BMC Systems Biology, October 2013
DOI 10.1186/1752-0509-7-107
Pubmed ID
Authors

Helen L Kotze, Emily G Armitage, Kieran J Sharkey, James W Allwood, Warwick B Dunn, Kaye J Williams, Royston Goodacre

Abstract

Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 133 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 3 2%
Switzerland 2 2%
Sweden 1 <1%
Hungary 1 <1%
Brazil 1 <1%
Germany 1 <1%
South Africa 1 <1%
Singapore 1 <1%
Iran, Islamic Republic of 1 <1%
Other 2 2%
Unknown 119 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 29%
Researcher 28 21%
Student > Master 22 17%
Professor 9 7%
Professor > Associate Professor 7 5%
Other 19 14%
Unknown 9 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 46 35%
Biochemistry, Genetics and Molecular Biology 19 14%
Chemistry 17 13%
Medicine and Dentistry 8 6%
Computer Science 7 5%
Other 23 17%
Unknown 13 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 November 2013.
All research outputs
#15,055,021
of 18,796,975 outputs
Outputs from BMC Systems Biology
#816
of 1,127 outputs
Outputs of similar age
#147,463
of 203,290 outputs
Outputs of similar age from BMC Systems Biology
#46
of 60 outputs
Altmetric has tracked 18,796,975 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,127 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.