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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Development of a protein classification model based on methods of topological data analysis
Creators: name: ÐÐ¸Ð½Ð´Ðµ, ÐÐ°Ð½Ð¸Ð¸Ð»
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: 2024
Subjects: ÑÐ¾Ð¿Ð¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ð¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
Ð¿ÐµÑÑÐ¸ÑÑÐµÐ½ÑÐ½Ð°Ñ Ð³Ð¾Ð¼Ð¾Ð»Ð¾Ð³Ð¸Ñ
ÑÑÑÑÐºÑÑÑÐ° Ð±ÐµÐ»ÐºÐ°
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÑÐµÐºÑÑÐ°
topological data analysis
persistent homology
protein structure
text classification

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description: This paper is about solution the problem of classifying a protein by letter sequences describing its primary and secondary structures, to determine whether it has the desired property or not. To do this, the available methods for solving the classification of text sequences were studied and the problem of class imbalance was discovered. To solve this problem, methods of topological data analysis were investigated to create a new dataset consisting of topological descriptors instead of text.Â  To do this, algorithms for their calculation were researched and different combinations of these descriptors were experimentally compared to form a new data set. Accordingly, an algorithm was proposed to reduce text sequences to samples with which topological representations can be compared. Next, a comparison was made of classical machine learning methods for classifying tabular data, including random forest, boosting and logistic regression. In the course of the work, a model was selected with parameters that give the best accuracy and the effect of protein length on the accuracy was studied.