title: ÐÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÐºÐ¾Ð½ÑÑÐ¾Ð»Ñ ÑÐ¾ÑÑÐ° Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ¸Ð¹ Ð¸Ð½Ð´ÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÑ Ð¿Ð»ÑÑÐ¸Ð¿Ð¾ÑÐµÐ½ÑÐ½ÑÑ ÐºÐ»ÐµÑÐ¾Ðº ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°
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: Machine learning methods to control the growth of human induced pluripotent stem cell populations
Creators: name: ÐÐ°Ð¼Ð°ÐµÐ²Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
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: 2022
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð±Ð¸Ð½Ð°ÑÐ½Ð°Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÐºÐ»ÐµÑÐ¾Ðº Ð¸ ÐºÐ¾Ð»Ð¾Ð½Ð¸Ð¹
ÑÐ²ÑÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¸Ð½Ð´ÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐµ Ð¿Ð»ÑÑÐ¸Ð¿Ð¾ÑÐµÐ½ÑÐ½ÑÐµ ÑÑÐ²Ð¾Ð»Ð¾Ð²ÑÐµ ÐºÐ»ÐµÑÐºÐ¸ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°
machine learning
binary classification
classification of cells and colonies
CNN
iPSCs

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description: The subject of the graduate qualification work is Â«Machine learning methods toÂ control the growth of human induced pluripotent stem cell populationsÂ».Â  Â  The paper formulated a classification task for the timely detection of coloniesÂ of human pluripotent cells with Â«badÂ» phenotypic properties. Previously publishedÂ papers covering current machine learning algorithms for classifying cell colonies inÂ images were studied. After a comparative analysis of the methods, convolutional neuralÂ networks were selected as the most preferred.Â  Â  In order to form a collection of images, photography was carried out for aÂ month using a fluorescent electron microscope at the Institute of Cytology of theÂ Russian Academy of Sciences. An expert biologist determined class labels based onÂ morphological parameters.Â  Â  In the course of numerical experiments, hyperparameters were optimally selectedÂ for the best model so that its accuracy was about83%. In the future, the positive impactÂ of data preprocessing and augmentation was demonstrated, with a subsequent increaseÂ in accuracy toÂ 84%Â andÂ 89%, respectively.Â  Â  During an additional study conducted as part of the final qualifying work, it wasÂ found that the most informative, in terms of classification quality, are images with aÂ linear size ofÂ â¼144Â Î¼?. Â  Â  This scale is intermediate between the characteristic diameterÂ of a single cellÂ â¼15Â Î¼?Â and the entire colonyÂ â¼540Â Î¼?.Â The results obtained indicate that the trained classifier is effective for predictingÂ the phenotypic properties of colonies. The software components developed in the courseÂ of research can later be used for non-invasive automated testing in order to identifyÂ undesirable developmental anomalies.Â Â