title: ÐÐ½Ð°Ð»Ð¸Ð· Ð²Ð»Ð¸ÑÐ½Ð¸Ñ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ°ÐºÑÐ¾ÑÐ¾Ð² Ð½Ð° ÑÐµÐ½Ð¾ÑÐ¸Ð¿ Ð¼Ð°ÑÐ°
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: Impact analysis of genetic factors on mung bean phenotype
Creators: name: ÐÐ¾ÑÑÐ¸Ð½Ð°, ÐÐ°Ð´ÐµÐ¶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: 2022
Subjects: Ð°Ð½Ð°Ð»Ð¸Ð·
Ð²Ð»Ð¸ÑÐ½Ð¸Ñ
Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¸Ðµ
ÑÐ°ÐºÑÐ¾ÑÑ
ÑÐµÐ½Ð¾ÑÐ¸Ð¿
Ð¼Ð°Ñ
gwas
vigna
abc
random forest

description: Ð Ð°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð»Ð½Ð¾Ð³ÐµÐ½Ð¾Ð¼Ð½Ð¾Ð³Ð¾ Ð¿Ð¾Ð¸ÑÐºÐ° Ð°ÑÑÐ¾ÑÐ¸Ð°ÑÐ¸Ð¹ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐ»Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ñ Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐµÐ»Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÑÑÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ Ð½Ð°ÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð²Ð°Ð¶Ð½Ð¾ÑÑÐ¸ ÐÐÐ Ð¿Ð¾ Ð¼Ð¾Ð´ÐµÐ»Ð¸, Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð½Ð° Ð´Ð°Ð½Ð½ÑÑ, ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÐ¸Ñ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ðµ Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ ÐÐÐ Ð¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ° ÑÐµÐ½Ð¾ÑÐ¸Ð¿Ð° Ð´Ð»Ñ Ð½ÐµÐºÐ¾ÑÐ¾ÑÐ¾Ð³Ð¾ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²Ð° ÑÐ°ÑÑÐµÐ½Ð¸Ð¹. ÐÐ¾Ð´Ð±Ð¾Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÑ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð¿ÑÐ¾Ð¸ÑÑÐ¾Ð´Ð¸Ñ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾Ñ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±Ð¾ÑÐµ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¾ ÑÑÐ¸ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð° Ð´Ð»Ñ Ð½Ð°ÑÐ¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð²Ð°Ð¶Ð½Ð¾ÑÑÐ¸ ÐÐÐ Ð¿Ð¾ Ð·Ð°Ð´Ð°Ð½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸: Ð²Ð°ÑÐ¶Ð½Ð¾ÑÑÑ, ÐºÐ°Ðº ÑÑÐ¼Ð¼Ð° ÑÐ¼ÐµÐ½ÑÑÐµÐ½Ð¸Ð¹ Ð·Ð°Ð³ÑÑÐ·Ð½ÐµÐ½Ð¸Ð¹, Ð·Ð½Ð°ÑÐµÐ½Ð¸Ñ Ð¨ÐµÐ¿Ð»Ð¸ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ ÐÐ¾ÑÑÑÑ. Ð¢Ð°ÐºÐ¶Ðµ Ð¿ÑÐµÐ´ÑÑÐ²Ð°Ð»ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´Ñ ÑÑÐµÑÐ° Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÑÑÑÐºÑÑÑÑ Ð´Ð°Ð½Ð½ÑÑ, Ð·Ð°ÐºÐ»ÑÑÐ°ÑÑÐ¸ÐµÑÑ Ð² ÑÐ¼ÐµÐ½ÑÑÐµÐ½Ð¸Ð¸ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸, ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ ÐºÐ¾ÑÑÐµÐºÑÐ¸ÑÐ¾Ð²ÐºÐµ Ð·Ð½Ð°ÑÐµÐ½Ð¸Ð¹ ÑÐµÐ½Ð¾ÑÐ¸Ð¿Ð° Ñ ÑÑÐµÑÐ¾Ð¼ ÐºÐ»Ð°ÑÑÐµÑÐ¾Ð². Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ð° ÐºÐ¾ÑÑÐµÐºÑÐ¸ÑÐ¾Ð²ÐºÐ° Ð´Ð°Ð½Ð½ÑÑ Ñ ÑÑÐµÑÐ¾Ð¼ Ð¿Ð¾Ð¿ÑÐ»ÑÑÐ¸Ð¾Ð½Ð½Ð¾Ð¹ ÑÑÑÑÐºÑÑÑÑ, Ð±ÑÐ»Ð¸ Ð½Ð°Ð¹Ð´ÐµÐ½Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐµ Ð³Ð¸Ð¿ÐµÑÐ¿Ð°ÑÐ°Ð¼ÐµÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð¿ÑÐ¸Ð±Ð»Ð¸Ð¶ÐµÐ½Ð½ÑÑ Ð±Ð°Ð¹ÐµÑÐ¾Ð²ÑÐºÐ¸Ñ Ð²Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ð¹, Ð±ÑÐ»Ð¸ Ð¾ÑÑÐµÑÐ½Ñ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¸, Ð¿ÑÐµÐ´Ð¿Ð¾Ð»Ð¾Ð¶Ð¸ÑÐµÐ»ÑÐ½Ð¾ Ð¸Ð¼ÐµÑÑÐ¸Ðµ Ð·Ð½Ð°ÑÐ¸Ð¼Ð¾Ðµ Ð²Ð»Ð¸ÑÐ½Ð¸Ðµ Ð½Ð° Ð¿ÑÐ¸Ð·Ð½Ð°Ðº ÑÐµÐ½Ð¾ÑÐ¸Ð¿Ð° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÑÐµÑ ÑÐ°Ð·Ð´Ð¸ÑÐ½ÑÑ Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¾Ð² Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð²Ð°Ð¶Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð², Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ðµ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ² Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð².
description: The work is devoted to conducting genome-wide association studies using machine learning models. in order to achieve this goal, we use importance scoring based on the pretrained model. Three different approaches had been used In order to provide importance scoring for SNP. Training data consists of a description of the sequence of SNPs and a phenotype trait for a certain set of plants. The selection of optimal hyperparameters occurs using the method of approximate Bayesian calculations. The paper presents three different approaches for finding the importance of SNPs for a given model: importance as the sum of pollution reductions, Shapley values, and Boruta's algorithm. Additionaly population structure should be taken in an account . In order to do this we apply dimension reduction algorithm to our data then make clustering and adjust the phenotype values according to clusters.In this work we corrected the data according to the population structue, found an optimal hyperparameters of the model using approximate bayesian computations, after that we chose importans SNPs using three different methods of importance scoring, then compared those methods.