title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð²ÑÐ±Ð¾ÑÐ° Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ð² Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³ÐµÐ½Ð¾Ð¼Ð½Ð¾Ð¹ ÑÐµÐ»ÐµÐºÑÐ¸Ð¸
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: A method for feature set optimization in genomic selection
Creators: name: ÐÐ¸Ð½ÐµÐµÐ², ÐÐ»ÐµÐºÑÐ°Ð½Ð´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: 2020
Subjects: Ð¾ÑÐ±Ð¾Ñ Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð´Ð»Ñ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸
Ð³ÐµÐ½Ð¾Ð¼Ð½Ð°Ñ ÑÐµÐ»ÐµÐºÑÐ¸Ñ
Ð±Ð¸Ð½Ð°ÑÐ½Ð°Ñ ÑÐ°Ð·Ð½Ð¾ÑÑÐ½Ð°Ñ ÑÐ²Ð¾Ð»ÑÑÐ¸Ñ
ÑÐ¾Ñ
Ð¿ÑÐµÐ½Ð¸ÑÐ°
feature selection for regression model
genomic selection
binary differential evolution
soybean
wheat

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description: This work is dedicated to the development of an algorithm for selecting a subset of variables characterizing the plant genome for predicting economically valuable traits of cultivated plants in a linear model of genomic selection. The creation of such models is complicated by the fact that the number of experiments is much less than the number of variables. Tasks that were solved during the study: 1. Studying existing genomic selection algorithms 2. Development of a modified binary difference evolution algorithm for finding the optimal subset of variables in the genomic selection algorithm. 3. Use of the algorithm with real soy and wheat data. The implemented algorithm consists of two stages. At the first stage, to create the feature pool for binary differential evolution, three filter methods are applied to the data: Fisher score, one-way analysis of variance, and the information gain. As a fitness function, a multi-trait model, ridge regression for the best linear unbiased prediction (rrBLUP), or the general Bayesian linear regression (BGLR) are used. A new algorithm during the tests on various data sets has shown its effectiveness and good potential in the tasks for feature set optimization in genomic selection.