Biomedical Engineering Reference
In-Depth Information
non-labeled and real-time assessment of cellular quality strongly required in the
present industrialization era of tissue engineering and regenerative medicine. Our
results, most of them are newly presented in this chapter, indicate that quantitative
prediction,
categorical
prediction,
and
multi-categorical
classification
can
be
achieved with high accuracy.
Like the other machine learning models in different research fields, robustness
should be repeatedly examined to build universally effective cell quality prediction
models. We are conducting further investigations to determine the extent of
cellular variations to build a practically useful model.
We strongly expect that the feedback loop of advances and improvements in
biology and computational technology will advance this field of cell assessment
with bioinformatics machine learning models.
Acknowledgments We are grateful to the New Energy and Industrial Technology Development
Organization (NEDO) for the Grant for Industrial Technology Research (Financial Support to
Young Researchers, 09C46036a) for the support. We also thank to the Nikon Corporation for
their research collaboration and financial support. We also thank Mai Okada and Yurika
Nonogaki for supporting the experiments and data storage. Finally we greatly thank Yoshihide
Nagura, Kazuhiro Mukaiyama, and Asuka Miwa for establishing basal techniques for our image
analysis procedure.
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