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Dynamic Network‐Based Relevance Score Reveals Essential Proteins and Functional Modules in Directed Differentiation

Overview of attention for article published in Stem Cells International, April 2015
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Article details
Title
Dynamic Network‐Based Relevance Score Reveals Essential Proteins and Functional Modules in Directed Differentiation
Published in
Stem Cells International, April 2015
DOI 10.1155/2015/792843
Pubmed ID
Authors
Abstract

The induction of stem cells toward a desired differentiation direction is required for the advancement of stem cell-based therapies. Despite successful demonstrations of the control of differentiation direction, the effective use of stem cell-based therapies suffers from a lack of systematic knowledge regarding the mechanisms underlying directed differentiation. Using dynamic modeling and the temporal microarray data of three differentiation stages, three dynamic protein-protein interaction networks were constructed. The interaction difference networks derived from the constructed networks systematically delineated the evolution of interaction variations and the underlying mechanisms. A proposed relevance score identified the essential components in the directed differentiation. Inspection of well-known proteins and functional modules in the directed differentiation showed the plausibility of the proposed relevance score, with the higher scores of several proteins and function modules indicating their essential roles in the directed differentiation. During the differentiation process, the proteins and functional modules with higher relevance scores also became more specific to the neuronal identity. Ultimately, the essential components revealed by the relevance scores may play a role in controlling the direction of differentiation. In addition, these components may serve as a starting point for understanding the systematic mechanisms of directed differentiation and for increasing the efficiency of stem cell-based therapies.

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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 3 60%
Professor > Associate Professor 1 20%
Student > Master 1 20%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 3 60%
Biochemistry, Genetics and Molecular Biology 1 20%
Computer Science 1 20%
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 16 May 2015.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from Stem Cells International
#1,004
of 1,317 outputs
Outputs of similar age
#240,323
of 279,759 outputs
Outputs of similar age from Stem Cells International
#30
of 37 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,317 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 1st percentile – i.e., 1% 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 279,759 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.