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Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization

Overview of attention for article published in BMC Systems Biology, March 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (58th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

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5 tweeters

Citations

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9 Dimensions

Readers on

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44 Mendeley
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1 CiteULike
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Title
Estimating genome-wide regulatory activity from multi-omics data sets using mathematical optimization
Published in
BMC Systems Biology, March 2017
DOI 10.1186/s12918-017-0419-z
Pubmed ID
Authors

Saskia Trescher, Jannes Münchmeyer, Ulf Leser

Abstract

Gene regulation is one of the most important cellular processes, indispensable for the adaptability of organisms and closely interlinked with several classes of pathogenesis and their progression. Elucidation of regulatory mechanisms can be approached by a multitude of experimental methods, yet integration of the resulting heterogeneous, large, and noisy data sets into comprehensive and tissue or disease-specific cellular models requires rigorous computational methods. Recently, several algorithms have been proposed which model genome-wide gene regulation as sets of (linear) equations over the activity and relationships of transcription factors, genes and other factors. Subsequent optimization finds those parameters that minimize the divergence of predicted and measured expression intensities. In various settings, these methods produced promising results in terms of estimating transcription factor activity and identifying key biomarkers for specific phenotypes. However, despite their common root in mathematical optimization, they vastly differ in the types of experimental data being integrated, the background knowledge necessary for their application, the granularity of their regulatory model, the concrete paradigm used for solving the optimization problem and the data sets used for evaluation. Here, we review five recent methods of this class in detail and compare them with respect to several key properties. Furthermore, we quantitatively compare the results of four of the presented methods based on publicly available data sets. The results show that all methods seem to find biologically relevant information. However, we also observe that the mutual result overlaps are very low, which contradicts biological intuition. Our aim is to raise further awareness of the power of these methods, yet also to identify common shortcomings and necessary extensions enabling focused research on the critical points.

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 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Unknown 43 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 34%
Researcher 5 11%
Student > Bachelor 4 9%
Professor > Associate Professor 3 7%
Student > Doctoral Student 3 7%
Other 7 16%
Unknown 7 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 23%
Computer Science 9 20%
Agricultural and Biological Sciences 7 16%
Medicine and Dentistry 3 7%
Engineering 2 5%
Other 5 11%
Unknown 8 18%

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 03 April 2017.
All research outputs
#4,062,484
of 9,339,536 outputs
Outputs from BMC Systems Biology
#315
of 914 outputs
Outputs of similar age
#106,341
of 259,880 outputs
Outputs of similar age from BMC Systems Biology
#9
of 24 outputs
Altmetric has tracked 9,339,536 research outputs across all sources so far. This one has received more attention than most of these and is in the 55th percentile.
So far Altmetric has tracked 914 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 63% 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 259,880 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 58% of its contemporaries.
We're also able to compare this research output to 24 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 62% of its contemporaries.