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Multiplex methods provide effective integration of multi-omic data in genome-scale models

Overview of attention for article published in BMC Bioinformatics, March 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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8 X users

Citations

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46 Dimensions

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112 Mendeley
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Title
Multiplex methods provide effective integration of multi-omic data in genome-scale models
Published in
BMC Bioinformatics, March 2016
DOI 10.1186/s12859-016-0912-1
Pubmed ID
Authors

Claudio Angione, Max Conway, Pietro Lió

Abstract

Genomic, transcriptomic, and metabolic variations shape the complex adaptation landscape of bacteria to varying environmental conditions. Elucidating the genotype-phenotype relation paves the way for the prediction of such effects, but methods for characterizing the relationship between multiple environmental factors are still lacking. Here, we tackle the problem of extracting network-level information from collections of environmental conditions, by integrating the multiple omic levels at which the bacterial response is measured. To this end, we model a large compendium of growth conditions as a multiplex network consisting of transcriptomic and fluxomic layers, and we propose a multi-omic network approach to infer similarity of growth conditions by integrating layers of the multiplex network. Each node of the network represents a single condition, while edges are similarities between conditions, as measured by phenotypic and transcriptomic properties on different layers of the network. We then fuse these layers into one network, therefore capturing a global network of conditions and the associated similarities across two omic levels. We apply this multi-omic fusion to an updated genome-scale reconstruction of Escherichia coli that includes underground metabolism and new gene-protein-reaction associations. Our method can be readily used to evaluate and cross-compare different collections of conditions among different species. Acquiring multi-omic information on the topology of the space of experimental conditions makes it possible to infer the position and to build condition-specific models of untested or incomplete profiles for which experimental data is not available. Our weighted network fusion method for genome-scale models is freely available at https://github.com/maxconway/SNFtool .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Germany 1 <1%
Unknown 110 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 32%
Student > Master 20 18%
Student > Bachelor 10 9%
Researcher 10 9%
Student > Doctoral Student 6 5%
Other 16 14%
Unknown 14 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 21 19%
Agricultural and Biological Sciences 21 19%
Computer Science 18 16%
Engineering 9 8%
Mathematics 5 4%
Other 20 18%
Unknown 18 16%
Attention Score in Context

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 12 April 2016.
All research outputs
#5,705,228
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#2,018
of 7,418 outputs
Outputs of similar age
#77,575
of 300,320 outputs
Outputs of similar age from BMC Bioinformatics
#42
of 128 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 72% 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 300,320 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 74% of its contemporaries.
We're also able to compare this research output to 128 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 67% of its contemporaries.