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Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods

Name: Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods
Creator: Vedeneeva, Ekaterina

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 30 Oct 2023Publisher:Zenodo

Authors: Vedeneeva, Ekaterina;

doi: 10.5281/zenodo.10052094 , 10.5281/zenodo.10052095

Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods

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Abstract

Research on various machine learning methods in order to find an effective model for classifying human pluripotent stem cell colonies by their quality using morphological parameters as predictors. The code works with dataset containing information about several morphological parameters and phenotypes obtained for cells and colonies from hESC line H9, hiPSC line AD3, and hiPSC line CaSR [Gursky, V. (2022). Dataset with values of morphological parameters and phenotypes of cells and colonies from three human pluripotent stem cell lines [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7150644]. This code is a supplementary material to the manuscript "Morphological Signal Processing for Phenotype Recognition of Human Pluripotent Stem Cells Using Machine Learning Methods" by "Ekaterina Vedeneeva, Vitaly Gursky, Maria Samsonova and Irina Neganova", submitted to the Biomedicines journal (MDPI).

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