title: ÐÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ ÑÐµÐ½Ð¾ÑÐ¸Ð¿Ð° ÐºÐ¾Ð»Ð¾Ð½Ð¸Ð¹ Ð¿Ð»ÑÑÐ¸Ð¿Ð¾ÑÐµÐ½ÑÐ½ÑÑ ÑÑÐ²Ð¾Ð»Ð¾Ð²ÑÑ ÐºÐ»ÐµÑÐ¾Ðº ÑÐµÐ»Ð¾Ð²ÐµÐºÐ° Ð¿Ð¾ Ð¼Ð¾ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ð¼ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ°Ð¼ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Determining the phenotype of human pluripotent stem cell colonies by morphological parameters using machine learning methods
Creators: name: ÐÐµÐ´ÐµÐ½ÐµÐµÐ²Ð°, ÐÐºÐ°ÑÐµÑÐ¸Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿Ð»ÑÑÐ¸Ð¿Ð¾ÑÐµÐ½ÑÐ½ÑÐµ ÑÑÐ²Ð¾Ð»Ð¾Ð²ÑÐµ ÐºÐ»ÐµÑÐºÐ¸ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°
Ð±Ð¸Ð½Ð°ÑÐ½Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
Ð¼ÐµÑÐ¾Ð´Ñ Ð¾ÑÐ±Ð¾ÑÐ° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð²
Ð¼Ð¾ÑÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ðµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÑ
machine learning
human pluripotent stem cells
binary classification
feature selection methods
morphological parameters

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description: The aim of this work is to use machine learning methods to construct, based on the available data, an effective model for classification of human pluripotent stem cell colonies, which allows to determine the colony phenotype (âgoodâ or âbadâ) using morphological parameters as predictors. In the course of the work, various methods of binary classification of objects by numerical characteristics were considered and the quality of their work was compared on cellular and colonial morphological data. After that, the analysis of feature selection methods was carried out and the effect of their application on the quality of the models was investigated. As a result, optimal colony morphology classification models were obtained with cross-validation accuracy of 68.11% and 76.10% for cellular and colony data, respectively. In addition, models were constructed using a combination of cellular and colonial parameters as predictors. The optimal model achieves a classification quality 98.28%, that shows the high efficiency of combining data on the morphology of the colony and cells inside it to determine the phenotype of the colony. Thus, the relationship between the morphology of colonies and cells with the phenotype of growing colonies was illustrated and classifiers were proposed to automatically determine the quality of colonies of human pluripotent stem cells.