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Integration of omics: more than the sum of its parts

Overview of attention for article published in Cancer & Metabolism, February 2016
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Title
Integration of omics: more than the sum of its parts
Published in
Cancer & Metabolism, February 2016
DOI 10.1186/s40170-016-0143-y
Pubmed ID
Authors

Joerg Martin Buescher, Edward M Driggers

Abstract

Genome scale data on biological systems has increasingly become available by sequencing of DNA and RNA, and by mass spectrometric quantification of proteins and metabolites. The cellular components from which these -omics regimes are derived act as one integrated system in vivo; thus, there is a natural instinct to integrate -omics data types. Statistical analyses, the use of previous knowledge in the form of networks, and the use of time-resolved measurements are three key design elements for life scientists to consider in planning integrated -omics studies. These design elements are reviewed in the context of multiple recent systems biology studies that leverage data from different types of -omics analyses. While most of these studies rely on well-established model organisms, the concepts for integrating -omics data that were developed in these studies can help to enable systems research in the field of cancer biology.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 255 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 1%
United Kingdom 2 <1%
Switzerland 1 <1%
Netherlands 1 <1%
Italy 1 <1%
France 1 <1%
Sweden 1 <1%
United States 1 <1%
Unknown 244 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 56 22%
Student > Ph. D. Student 53 21%
Student > Master 34 13%
Student > Bachelor 29 11%
Student > Doctoral Student 15 6%
Other 26 10%
Unknown 42 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 74 29%
Agricultural and Biological Sciences 68 27%
Computer Science 18 7%
Medicine and Dentistry 12 5%
Engineering 7 3%
Other 29 11%
Unknown 47 18%
Attention Score in Context

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 24 February 2016.
All research outputs
#17,286,379
of 25,374,647 outputs
Outputs from Cancer & Metabolism
#155
of 225 outputs
Outputs of similar age
#188,794
of 312,137 outputs
Outputs of similar age from Cancer & Metabolism
#5
of 6 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 225 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 25th percentile – i.e., 25% 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 312,137 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.