Machine Learning Models of Phase Contrast Images Predict Efficiency of Human Pluripotent Stem Cell Differentiation to Cardiomyocytes.

Feeney AK, Ovadia Y, Simmons AD, Elabd C, Peplinski CJ, Li F, Palecek SP

DOI: 10.1002/bit.70241
Published: 2026 May 14
Container: Biotechnology and bioengineering
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1002/bit.70241,
  title = {Machine Learning Models of Phase Contrast Images Predict Efficiency of Human Pluripotent Stem Cell Differentiation to Cardiomyocytes.},
  author = {Feeney AK and Ovadia Y and Simmons AD and Elabd C and Peplinski CJ and Li F and Palecek SP},
  year = {2026},
  journal = {Biotechnology and bioengineering},
  doi = {10.1002/bit.70241},
  url = {https://doi.org/10.1002/bit.70241}
}

RIS

TY  - JOUR
TI  - Machine Learning Models of Phase Contrast Images Predict Efficiency of Human Pluripotent Stem Cell Differentiation to Cardiomyocytes.
AU  - Feeney AK
AU  - Ovadia Y
AU  - Simmons AD
AU  - Elabd C
AU  - Peplinski CJ
AU  - Li F
AU  - Palecek SP
PY  - 2026
JO  - Biotechnology and bioengineering
DO  - 10.1002/bit.70241
UR  - https://doi.org/10.1002/bit.70241
ER  -

APA

AK, F., Y, O., AD, S., C, E., CJ, P., F, L., & SP, P. (2026). Machine Learning Models of Phase Contrast Images Predict Efficiency of Human Pluripotent Stem Cell Differentiation to Cardiomyocytes.. Biotechnology and bioengineering. https://doi.org/10.1002/bit.70241

Source records

pubmed · retrieved 2026-09-26T05:28:57.884Z