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Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells

Overview of attention for article published in PLoS Computational Biology, December 2011
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3 CiteULike
Title
Systematic Search for Recipes to Generate Induced Pluripotent Stem Cells
Published in
PLoS Computational Biology, December 2011
DOI 10.1371/journal.pcbi.1002300
Pubmed ID
Authors

Rui Chang, Robert Shoemaker, Wei Wang

Abstract

Generation of induced pluripotent stem cells (iPSCs) opens a new avenue in regenerative medicine. One of the major hurdles for therapeutic applications is to improve the efficiency of generating iPSCs and also to avoid the tumorigenicity, which requires searching for new reprogramming recipes. We present a systems biology approach to efficiently evaluate a large number of possible recipes and find those that are most effective at generating iPSCs. We not only recovered several experimentally confirmed recipes but we also suggested new ones that may improve reprogramming efficiency and quality. In addition, our approach allows one to estimate the cell-state landscape, monitor the progress of reprogramming, identify important regulatory transition states, and ultimately understand the mechanisms of iPSC generation.

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X Demographics

The data shown below were collected from the profiles of 9 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 137 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 7%
Portugal 1 <1%
France 1 <1%
Germany 1 <1%
Korea, Republic of 1 <1%
Ireland 1 <1%
Unknown 123 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 39 28%
Researcher 27 20%
Student > Master 12 9%
Student > Bachelor 12 9%
Professor > Associate Professor 11 8%
Other 21 15%
Unknown 15 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 68 50%
Biochemistry, Genetics and Molecular Biology 18 13%
Computer Science 7 5%
Physics and Astronomy 5 4%
Engineering 5 4%
Other 18 13%
Unknown 16 12%