title: ÐÐ¾Ð´ÐµÐ»Ð¸ Ð³ÐµÐ½Ð¾Ð¼Ð½Ð¾Ð¹ ÑÐµÐ»ÐµÐºÑÐ¸Ð¸ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð² Ñ Ð¿Ð¾Ð»Ð¸Ð¿Ð»Ð¾Ð¹Ð´Ð½ÑÑ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
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: Models of genomic selection for multiple traits in polyploid plants using artificial imaging
Creators: name: Ð¢ÑÑÑÐºÐ¸Ð½, Ð¯ÑÐ¾ÑÐ»Ð°Ð²
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: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ·ÑÐºÐ¸
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐºÐ°ÑÑÑ Ð°ÐºÑÐ¸Ð²Ð°ÑÐ¸Ð¸
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐµÐ³ÑÐµÑÑÐ¸Ð¾Ð½Ð½ÑÐµ Ð¼Ð¾Ð´ÐµÐ»Ð¸
deep learning
convolution neural network
activation maps
regression models

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description: In this work, we developed a model for predicting pairs of quantitative traits in wheat based on data containing information on genetic markers and phenotypic indices of plants. The models were convolutional neural networks with the ability to select the most significant genetic traits and were trained using a modified representation of SNPs. The main goal of this work is to develop a model for predicting two phenotypic traits from genetic data of hexaploid wheat represented in the form of artificial images. To achieve this goal, it is necessary to implement the architecture of a convolutional neural network, adapt the algorithm for encoding genetic data on single-nucleotide polymorphisms into artificial images taking into account the peculiarities of the crop, refine the algorithm for constructing maps of trait importance to calculate the most important markers for phenotype prediction, and apply the developed model to predict several quantitative indicators of wheat and select the most important markers, and then analyze the results. Numerical experiments produced models predicting three pairs of phenotypic traits, some of which were comparable in prediction quality to existing models used on the same data. The proposed model architecture, as well as the algorithm for translating tabular data into a visual representation with an algorithm for constructing importance maps, can be further applied in other scientific fields unrelated to biology and genetics.