Patient-Specific Induced Pluripotent Stem Cell Models: Characterization of iPS Cell-Derived Cardiomyocytes

2014 ◽  
pp. 343-353 ◽  
Author(s):  
Toru Egashira ◽  
Shinsuke Yuasa ◽  
Shugo Tohyama ◽  
Yusuke Kuroda ◽  
Tomoyuki Suzuki ◽  
...  
Keyword(s):  
Stem Cell ◽  
Pluripotent Stem Cell ◽  
Induced Pluripotent Stem Cell ◽  
Patient Specific ◽  
Cell Models ◽  
Ips Cell ◽  
Induced Pluripotent ◽  
Stem Cell Models

scholarly journals Patient-Specific Induced Pluripotent Stem-Cell Models for Long-QT Syndrome

2010 ◽  
Vol 363 (15) ◽  
pp. 1397-1409 ◽  
Author(s):  
Alessandra Moretti ◽  
Milena Bellin ◽  
Andrea Welling ◽  
Christian Billy Jung ◽  
Jason T. Lam ◽  
...  
Keyword(s):  
Stem Cell ◽  
Long Qt Syndrome ◽  
Pluripotent Stem Cell ◽  
Induced Pluripotent Stem Cell ◽  
Patient Specific ◽  
Cell Models ◽  
Long Qt ◽  
Qt Syndrome ◽  
Induced Pluripotent ◽  
Stem Cell Models

scholarly journals Epigenetic Characterization of the FMR1 Gene and Aberrant Neurodevelopment in Human Induced Pluripotent Stem Cell Models of Fragile X Syndrome

PLoS ONE ◽  
2011 ◽  
Vol 6 (10) ◽  
pp. e26203 ◽  
Author(s):  
Steven D. Sheridan ◽  
Kraig M. Theriault ◽  
Surya A. Reis ◽  
Fen Zhou ◽  
Jon M. Madison ◽  
...  
Keyword(s):  
Stem Cell ◽  
Fragile X Syndrome ◽  
Pluripotent Stem Cell ◽  
Fragile X ◽  
Induced Pluripotent Stem Cell ◽  
Fmr1 Gene ◽  
Cell Models ◽  
Induced Pluripotent ◽  
Stem Cell Models

scholarly journals Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Henry Joutsijoki ◽  
Markus Haponen ◽  
Jyrki Rasku ◽  
Katriina Aalto-Setälä ◽  
Martti Juhola
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Pluripotent Stem Cell ◽  
Disease Modeling ◽  
Induced Pluripotent Stem Cell ◽  
Patient Specific ◽  
Support Vector ◽  
Ips Cell ◽  
Cell Technology ◽  
Induced Pluripotent

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient’s cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using ak-NN classifier showing improved accuracy compared to earlier studies.

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