## 机器学习代写|机器学习代写machine learning代考|COMP5318

2022年10月13日

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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## 机器学习代写|机器学习代写machine learning代考|Marker Depuration

First, we will define markers and their importance. Markers are beneficial in the construction of precise genetic relationships, for parental determination and for the identification and mapping of quantitative trait loci (QTL). Between 1970 and 2001, most of the genetic progress in the livestock industry was reached by using pedigree and phenotypic information. However, after the first draft of the human genome project was finished in 2001 (The International SNP Map Working Group 2001), the cost of genotyping using single nucleotide polymorphisms (SNPs) started to decrease considerably, and now its cost is at least 1000 times lower. For this reason, Stonecking (2001) points out that SNPs have become the bread and butter of DNA sequence variation and are essential in determining the genetic potential of livestock and plant breeding.

However, it is also important to point out that other types of DNA markers have been discovered, such as restriction fragment length polymorphisms (RFLP), simple sequence repeat (SSR), Diversity Arrays Technology (DArT), simple sequence length polymorphisms (SSLP), amplified fragment length polymorphisms (AFLP), etc. However, SNPs have become the main markers used to detect DNA variation for some of the following reasons: (a) SNPs are abundant and found throughout the entire genome, in intragenic and extragenic regions (Schork et al. 2000), (b) they represent the most common genetic variants, (c) the location in the DNA: they are found in introns, exons, promoters, enhancers, or intergenic regions, (d) they are easily evaluated by automated means, (e) many of them have direct repercussions on traits of interest in plant and animals, (f) they are generally biallelic, and ( $g)$ they are now cheap and easy to genotype.

It is important to remember that DNA (deoxyribonucleic acid) is organized in pairs of chromosomes, each inherited from one of the parents. The diversity found among organisms is a result of variations in DNA sequences and of environmental effects. Genetic variation is substantial and each individual of a species, with the exception of monozygotic twins, possesses a unique DNA sequence. DNA variations are mutations ressulting from the substitution of single nucleotides (single nucleotide polymorphisms-SNPs), the insertion or deletion of DNA fragments of various lengths (from a single to several thousand nucleotides), or the duplication or inversion of DNA fragments (Marsjan and Oldenbroek 2007). For this reason, the genome is composed of four different nucleotides $(\mathrm{A}, \mathrm{C}, \mathrm{T}$, and $\mathrm{G})$. Next, we provide two important definitions that are keys to understanding how markers are used in genomic selection.

## 机器学习代写|机器学习代写machine learning代考|Methods to Compute the Genomic Relationship Matrix

The three methods described here to calculate the genomic relationship matrix (GRM) are based on VanRaden’s (2008) paper “Efficient methods to compute genomic predictions” where more theoretical support for each of these methods can be found. We assume that we have a matrix of markers of order $J \times p$, where $J$ denotes the number of lines and $p$ the number of markers, and that this matrix does not contain missing values and is coded as 0,1 , and 2 , or $-1,0$, and 1 to refer homozygotes major allele, heterozygous, and homozygous minor allele, respectively. Note that the last codification is related to the first by the relation $\boldsymbol{X}_2=$ $\boldsymbol{X}+\mathbf{1}_J \mathbf{1}_p^{\mathrm{T}}$, where $\boldsymbol{X}_2$ is a matrix of markers information coded in terms of $-1,0$, and 1, while $\boldsymbol{X}$ is the coded marker information in terms of 0 , 1, and 2 , and $\mathbf{1}_q$ is the column vector of dimension $q$ with ones in all its entries.
Method 1. This method calculates the GRM as
$$\boldsymbol{G}=\frac{1}{p} \boldsymbol{X} \boldsymbol{X}^{\mathrm{T}}$$
where $\boldsymbol{X}$ is the matrix of marker genotypes of dimensions $J \times p$. When the marker information is coded as $-1,0$, and 1 as described before, the diagonal terms of $p \boldsymbol{G}$ count the number of homozygous loci for each line, and the off-diagonal of $p \boldsymbol{G}$ is a measure of the number of alleles shared by two lines (VanRaden 2008).

# 机器学习代考

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## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。