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Estimating drivers of cell state transitions using gene regulatory network models

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

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

Mentioned by

twitter
24 tweeters

Citations

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10 Dimensions

Readers on

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44 Mendeley
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Title
Estimating drivers of cell state transitions using gene regulatory network models
Published in
BMC Systems Biology, December 2017
DOI 10.1186/s12918-017-0517-y
Pubmed ID
Authors

Daniel Schlauch, Kimberly Glass, Craig P. Hersh, Edwin K. Silverman, John Quackenbush

Abstract

Specific cellular states are often associated with distinct gene expression patterns. These states are plastic, changing during development, or in the transition from health to disease. One relatively simple extension of this concept is to recognize that we can classify different cell-types by their active gene regulatory networks and that, consequently, transitions between cellular states can be modeled by changes in these underlying regulatory networks. Here we describe MONSTER, MOdeling Network State Transitions from Expression and Regulatory data, a regression-based method for inferring transcription factor drivers of cell state conditions at the gene regulatory network level. As a demonstration, we apply MONSTER to four different studies of chronic obstructive pulmonary disease to identify transcription factors that alter the network structure as the cell state progresses toward the disease-state. We demonstrate that MONSTER can find strong regulatory signals that persist across studies and tissues of the same disease and that are not detectable using conventional analysis methods based on differential expression. An R package implementing MONSTER is available at github.com/QuackenbushLab/MONSTER.

Twitter Demographics

The data shown below were collected from the profiles of 24 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 %
Italy 1 2%
Unknown 43 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 36%
Researcher 6 14%
Student > Bachelor 4 9%
Professor 4 9%
Professor > Associate Professor 3 7%
Other 8 18%
Unknown 3 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 34%
Agricultural and Biological Sciences 15 34%
Computer Science 4 9%
Medicine and Dentistry 3 7%
Neuroscience 2 5%
Other 3 7%
Unknown 2 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 08 May 2018.
All research outputs
#1,927,946
of 17,366,233 outputs
Outputs from BMC Systems Biology
#67
of 1,115 outputs
Outputs of similar age
#62,279
of 417,692 outputs
Outputs of similar age from BMC Systems Biology
#7
of 81 outputs
Altmetric has tracked 17,366,233 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,115 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 93% 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 417,692 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 81 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.