title: ÐÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½ÑÐµ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ðµ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ñ ÑÐ°Ð·Ð½Ð¾ÑÑÐ½Ð¾Ð¹ ÑÐ²Ð¾Ð»ÑÑÐ¸ÐµÐ¹ Ð´Ð»Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÐ½Ð¾ÑÐ¸Ð¿Ð° ÑÐ°ÑÑ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: Approximate Bayesian computation with differential evolution for plant phenotype modeling
Creators: name: Ð¡Ð°Ð»Ð¸ÑÐ¾Ð², Ð¡ÐµÑÐ³ÐµÐ¹
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: 2023
Subjects: ÐÐ°ÑÐµÐ¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ðµ Ð¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
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Ð¾Ð´Ð½Ð¾Ð½ÑÐºÐ»ÐµÐ¾ÑÐ¸Ð´Ð½ÑÐ¹ Ð¿Ð¾Ð»Ð¸Ð¼Ð¾ÑÑÐ¸Ð·Ð¼
approximate bayesian computation
differential evolution
maximum mean discrepancy
single nucleotide polymorphism

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½ÑÑ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹ Ñ ÑÐ°Ð·Ð½Ð¾ÑÑÐ½Ð¾Ð¹ ÑÐ²Ð¾Ð»ÑÑÐ¸ÐµÐ¹. ÐÐ°Ð½Ð½ÑÐ¹ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÑÑÑ Ðº Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ ÑÐ°ÑÑÐµÐ½Ð¸Ð¹. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ Ð¼Ð¾Ð¶Ð½Ð¾ Ð¿Ð¾ÑÑÑÐ¾Ð¸ÑÑ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ñ Ð¿Ð¾ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐ¼ ÑÐ°ÑÑÐµÐ½Ð¸ÑÐ¼, ÑÑÐ¾ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ñ Ð´ÐµÐ»Ð°ÑÑ Ð²ÑÐ²Ð¾Ð´Ñ Ð¾ ÐºÐ°ÑÐµÑÑÐ²Ð°Ñ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ ÑÐ¾ÑÑÐ¾Ð². ÐÑÐ¾ÑÑÐ°Ñ ÑÐ¸Ð¼ÑÐ»ÑÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ(Simple Simulation Model) - Ð´Ð¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐ²ÐµÑÐµÐ½Ð¸Ñ. ÐÐ¾Ð´Ð¸ÑÐ¸ÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ ÑÑÐ¸ÑÑÐ²Ð°ÑÑ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÑÑ Ð¸Ð½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ Ð¸ Ð¿Ð¾Ð´Ð±Ð¸ÑÐ°ÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐµ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½ÑÐµ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸. ÐÑÐ±Ð¾Ñ ÑÑÐ½ÐºÑÐ¸Ð¾Ð½Ð°Ð»ÑÐ½ÑÑ Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐµÐ¹ Ð¾ÑÑÑÐµÑÑÐ²Ð»ÑÐµÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð³ÑÐ°Ð¼Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð¹ ÑÐ²Ð¾Ð»ÑÑÐ¸Ð¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÑÐ¾ÑÐ°ÑÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸. ÐÑÐ¾ÑÑÐ°Ñ ÑÐ¸Ð¼ÑÐ»ÑÑÐ¸Ð¾Ð½Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð² ÑÐµÐ±Ñ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÑ, Ð²ÑÐ±Ð¾Ñ ÐºÐ¾ÑÐ¾ÑÑÑ Ð¾ÑÑÑÐµÑÑÐ²Ð»ÑÐµÑÑÑ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½ÑÑ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹. ÐÐ»Ñ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÑÐºÐ¾ÑÐ¾ÑÑÐ¸ ÑÑÐ¾Ð´Ð¸Ð¼Ð¾ÑÑÐ¸ Ð¸ ÑÐ¾ÑÐ½Ð¾ÑÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð° Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½ÑÑ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹ Ð±ÑÐ»Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ Ð¼ÐµÑÑÐ¸ÐºÐ¸, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ¸Ðµ Ð½Ð°ÑÐ¾Ð´Ð¸ÑÑ ÑÐ°ÑÑÑÐ¾ÑÐ½Ð¸Ðµ Ð¼ÐµÐ¶Ð´Ñ Ð´Ð²ÑÐ¼Ñ Ð²ÑÐ±Ð¾ÑÐºÐ°Ð¼Ð¸. ÐÑÐ»Ð¸ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ñ Ð¼ÐµÑÑÐ¸ÐºÐ¸ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸, Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¸ Ð¼Ð°ÐºÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ ÑÑÐµÐ´Ð½ÐµÐ³Ð¾ ÑÐ°ÑÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ. ÐÐ°Ð½Ð½ÑÐµ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð±ÑÐ»Ð¸ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ Ð½Ð° ÑÐ·ÑÐºÐµ C++ Ð¸ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ñ Ðº Ð´Ð°Ð½Ð½ÑÐ¼ Ð¿Ð¾ Ð²Ð¸Ð³Ð½Ðµ.
description: In this paper, an algorithm of approximate bayesian computation with differential evolution is considered and applied. This algorithm is applied to the optimization of a dynamic model for predicting the flowering time of plants. Based on the results of the dynamic model of forecasting the flowering time, it is possible to make forecasts for various plants, which will allow us to draw conclusions about the qualities of different varieties. A Simple Simulation Model is a dynamic model for predicting the flowering time. The modified model allows taking into account genetic information and selecting optimal functional dependencies. The choice of functional dependencies is based on the method of grammatical evolution using the stochastic optimization method. simple simulation model includes hyperparameters, the choice of which is based on the method of approximate bayesian computation. To increase the convergence rate and accuracy of the method of approximate bayesian computation, metrics were developed that allow finding the distance between two samples. Metrics were built based on a convolutional neural network, a deep neural network and a maximum mean discrepancy. These modifications were developed in C++ and applied to the vigna data.