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Unwinding the Novel Genes Involved in the Differentiation of Embryonic Stem Cells into Insulin-Producing Cells: A Network-Based Approach

Overview of attention for article published in Interdisciplinary Sciences: Computational Life Sciences, February 2016
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Mentioned by

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1 tweeter
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1 Facebook page

Citations

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1 Dimensions

Readers on

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7 Mendeley
Title
Unwinding the Novel Genes Involved in the Differentiation of Embryonic Stem Cells into Insulin-Producing Cells: A Network-Based Approach
Published in
Interdisciplinary Sciences: Computational Life Sciences, February 2016
DOI 10.1007/s12539-016-0148-9
Pubmed ID
Authors

T. Femlin Blessia, Sachidanand Singh, J. Jannet Vennila

Abstract

Diabetes is one of the main causes of death in the world. Diabetes is marked by high blood glucose levels and develops when the body doesn't produce enough insulin or is not able to use insulin effectively, or both. Type I diabetes is a chronic sickness caused by lack of insulin due to the autoimmune destruction of pancreatic insulin-producing beta cells. Research on permanent cure for diabetes is in progress with several remarkable findings in the past few decades among which stem cell therapy has turned out to be a promising way to cure diabetes. Stem cells have the remarkable potential to differentiate into glucose-responsive beta cells through controlled differentiation protocols. Discovering novel targets that could potentially influence the differentiation to specific cell type will help in disease therapy. The present work focuses on finding novel genes or transcription factors involved in the human embryonic stem cell differentiation into insulin-producing beta cells using network biology approach. The interactome of 321 genes and their associated molecules involved in human embryonic stem cell differentiation into beta cells was constructed, which includes 1937 nodes and 8105 edges with a scale-free topology. Pathway analysis for the hubs obtained through MCODE revealed that four highly interactive hubs were relevant to embryonic stem cell differentiation into insulin-producing cells. Their role in different pathways and stem cell differentiation was studied. Centrality parameters were applied to identify the potential controllers of the differentiation processes: BMP4, SALL4, ZIC1, NTS, RNF2, FOXO1, AKT1 and GATA4. This type of approach gives an insight to identify potential genes/transcription factors which may play influential role in many complex biological processes.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 14%
Unknown 6 86%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 29%
Student > Doctoral Student 2 29%
Researcher 2 29%
Professor > Associate Professor 1 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 43%
Agricultural and Biological Sciences 1 14%
Unspecified 1 14%
Medicine and Dentistry 1 14%
Engineering 1 14%
Other 0 0%

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 22 February 2016.
All research outputs
#9,608,334
of 12,511,869 outputs
Outputs from Interdisciplinary Sciences: Computational Life Sciences
#67
of 154 outputs
Outputs of similar age
#209,728
of 335,236 outputs
Outputs of similar age from Interdisciplinary Sciences: Computational Life Sciences
#4
of 13 outputs
Altmetric has tracked 12,511,869 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 154 research outputs from this source. They receive a mean Attention Score of 2.0. This one has gotten more attention than average, scoring higher than 52% 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 335,236 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 13 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 69% of its contemporaries.