title: ÐÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð², Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð½ÑÑ Ð¿ÑÑÐ¼ÑÐ¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸, Ð´Ð»Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð·Ð°Ð´Ð°Ñ Ð±Ð¸Ð¾Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸ÐºÐ¸
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: Utilization of Synthetic Images and Direct Feature Extraction Methods for Addressing Bioinformatics Challenges
Creators: name: ÐÑÐ±ÑÐ¸ÐµÐ½ÐºÐ¾, ÐÐµÐ½Ð¸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: 2024
Subjects: ÑÐ¸Ð½ÑÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐÐ
Ð½ÑÑ
ÑÐµÐ½Ð¾ÑÐ¸Ð¿
Gradient Boost
Random Forest
XGBoost
Catboost
synthetic images
machine learning
phenotyping
peanuts
phenotype

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description: Topic of final qualifying work: "Utilization of Synthetic Images and Direct Feature Extraction Methods for Addressing Bioinformatics Challenges". The thesis focuses on tackling the problem of effectively solving the phenotype prediction task in light of the growing demand to develop agricultural crop varieties with higher yield potential and resilience to abiotic stresses. One of the key challenges is overcoming the "curse of dimensionality" associated with analyzing data characterized by a small number of samples and a large number of features. The aim of the thesis is to investigate modern approaches to training models with such data to achieve high prediction accuracy while significantly speeding up the training process. The study explores contemporary methods of training ensemble regression models, with particular emphasis on assessing the effectiveness of using synthetic images to enhance model training. Additionally, an analysis of metrics characterizing model improvement is conducted. As a result of the research, regression models capable of predicting phenotypic traits for peanuts based on their genetic information have been trained. These models demonstrate high prediction accuracy with a significant reduction in training time, making them promising tools for crop breeding selection.