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Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data

Overview of attention for article published in BMC Systems Biology, December 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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1 blog
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2 X users

Citations

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16 Dimensions

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31 Mendeley
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Title
Identification of network-based biomarkers of cardioembolic stroke using a systems biology approach with time series data
Published in
BMC Systems Biology, December 2015
DOI 10.1186/1752-0509-9-s6-s4
Pubmed ID
Authors

Yung-Hao Wong, Chia-Chou Wu, Hsien-Yong Lai, Bo-Ren Jheng, Hsing-Yu Weng, Tzu-Hao Chang, Bor-Sen Chen

Abstract

Molecular signaling of angiogenesis begins within hours after initiation of a stroke and the following regulation of endothelial integrity mediated by growth factor receptors and vascular growth factors. Recent studies further provided insights into the coordinated patterns of post-stroke gene expressions and the relationships between neurodegenerative diseases and neural function recovery processes after a stroke. Differential protein-protein interaction networks (PPINs) were constructed at 3 post-stroke time points, and proteins with a significant stroke relevance value (SRV) were discovered. Genes, including UBC, CUL3, APP, NEDD8, JUP, and SIRT7, showed high associations with time after a stroke, and Ingenuity Pathway Analysis results showed that these post-stroke time series-associated genes were related to molecular and cellular functions of cell death, cell survival, the cell cycle, cellular development, cellular movement, and cell-to-cell signaling and interactions. These biomarkers may be helpful for the early detection, diagnosis, and prognosis of ischemic stroke. This is our first attempt to use our theory of a systems biology framework on strokes. We focused on 3 key post-stroke time points. We identified the network and corresponding network biomarkers for the 3 time points, further studies are needed to experimentally confirm the findings and compare them with the causes of ischemic stroke. Our findings showed that stroke-associated biomarker genes at different time points were significantly involved in cell cycle processing, including G2-M, G1-S and meiosis, which contributes to the current understanding of the etiology of stroke. We hope this work helps scientists reveal more hidden cellular mechanisms of stroke etiology and repair processes.

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X Demographics

The data shown below were collected from the profiles of 2 X users 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 31 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 29%
Researcher 6 19%
Student > Bachelor 4 13%
Other 2 6%
Student > Master 2 6%
Other 3 10%
Unknown 5 16%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 19%
Medicine and Dentistry 5 16%
Neuroscience 4 13%
Computer Science 4 13%
Agricultural and Biological Sciences 3 10%
Other 2 6%
Unknown 7 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 18 January 2016.
All research outputs
#4,025,676
of 22,835,198 outputs
Outputs from BMC Systems Biology
#120
of 1,142 outputs
Outputs of similar age
#67,607
of 389,036 outputs
Outputs of similar age from BMC Systems Biology
#3
of 47 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one has done well, scoring higher than 89% 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 389,036 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 82% of its contemporaries.
We're also able to compare this research output to 47 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 93% of its contemporaries.