### 统计代写|网络分析代写Network Analysis代考|CSE416a

statistics-lab™ 为您的留学生涯保驾护航 在代写网络分析Network Analysis方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写网络分析Network Analysis代写方面经验极为丰富，各种代写网络分析Network Analysis相关的作业也就用不着说。

• 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

## 统计代写|网络分析代写Network Analysis代考|Organization of the book

We organize our book into the following chapters:

• Chapter 2: We introduce mathematical graph and properties. A graph is the basis of the entire graph theoretic modeling and analysis of biological networks. We even discuss the R scripting for handling graph data structures, briefly.
• Chapter 3: Various algorithms popularly studied in graph theory, such as graph traversal algorithms are discussed. In a biological network, power graph analysis is an important graph analysis method that we discuss with examples. Also, various node centrality measures are introduced and demonstrated with the help of $\mathrm{R}$ scripts.
• Chapter 4: Real-world networks follow certain special topological properties, which makes them different from the usual graph. Accordingly, they are classified into various network models. We use different models and their properties, and implement them using the R package.
• Chapter 5: The sources of three biological network repositories, which are publicly available databases, are discussed. The chapter starts with a basic introduction to popular and recently used database formats. It is a resourceful chapter for the biological network-related researches.
• Chapter 6: Gene expression networks have been introduced along with data generation sources for the expression networks. The overall discussion has been divided into two parts, in-silico network inference and post inference analysis. How gene network modules can be identified and how to rank important genes in an expression network has been discussed in the light of various algorithms. We even discuss various online and offline software tools to carry out gene expression network inference and analysis.
• Chapter 7: Protein and their physical interaction networks are vital to establishing true macromolecular connectivity in biological systems. How such interactions can be generated experimentally and predicted computationally has been highlighted. Recently, protein network alignment has gained importance in comparative network analysis for finding evolutionarily conserved proteins, which we include in this chapter. Few of the algorithms dealing with functional protein complex detection is discussed.
• Chapter 8: Finally, we introduce brain connectome networks with the input data sources and present trends in brain connectome network analysis.

## 统计代写|网络分析代写Network Analysis代考|Basic concepts

A graph [3] is a pictorial representation of a set of objects and their association with each other. The objects are popularly termed as nodes or vertices, and the associations are depicted using interconnections between pair of nodes, called edges. Mathematically, graphs are represented as a set of edges and vertices.
Definition 2.1.1 (Graph). A graph $\mathcal{G}$ is a pair of finite set of vertices and edges, $\mathcal{G}=(\mathcal{V}, \mathcal{E})$, such that $\mathcal{V}=\left{v_1, v_2, \cdots, v_n\right}$ and $\mathcal{E}=$ $\left{e_1, e_2, \cdots, e_m\right}$. An edge $e_k=\left(v_i, v_j\right)$ connects vertices $v_i$ and $v_j$

In the graph (Fig. 2.1), $\mathcal{V}={A, B, C, D, E, F}$ and $\mathcal{E}={(A, B)$, $(B, C),(C, D),(C, E),(E, E),(E, F),(E, D),(F, B)}$, where edges are an unordered pair of nodes having interconnections among them. Graph $\mathcal{G}$ is termed as undirected graph. The node $E$ is connected with itself through loop edge. A graph without my loop structure is called a simple graph.

A graph with an ordered pair of nodes, where edges are associated with directions is called a directed graph or digraph.

Definition 2.1.2 (Directed graph). A directed graph $\mathcal{G}=(\mathcal{V}, \mathcal{E})$ is a set of vertices $\mathcal{V}$ and edges $\mathcal{E}$, such that, for any edge $\left(v_i, v_j\right)$ posses direction denoted by arrow. Unlike undirected graph, for any edge $v_i \rightarrow v_j$, the edge $\left(v_i, v_j\right) \neq\left(v_j, v_i\right)$. The node $v_i$ is called tail, and $v_j$ is referred to as head of the edge $v_i \rightarrow v_j$. For example, see Fig. 2.2.
Definition 2.1.3 (Path). A path is a sequence of distinct vertices that are connected by edges. In other words, given a set of vertices, $\left{v_1, v_2, \cdots, v_k\right} \in \mathcal{G}(\mathcal{V})$ is a path if for every pair of vertices $v_i$ and $v_{i+1}$ have an edge $\left(v_i, v_{i+1}\right) \in \mathcal{G}(\mathcal{E})$. However, in case of a directed graph, a directed path connects the sequence of vertices with the added restriction that all edges are oriented towards the same direction.

In a path, if sequences of vertices are not distinct, it is referred to as a walk.

Two nodes, $v_i$ and $v_j$, are reachable from each other if there is a path that exists between $v_i$ and $v_j$.

A path is called a closed path or cycle if two terminal nodes, $v_1$ and $v_k$, are connected in a path, i.e., $\left(v_k, v_1\right) \in \mathcal{G}(\mathcal{E})$.

## 统计代写|网络分析代写Network Analysis代考|Organization of the book

• 第 2 章：我们介绍数学图和属性。图是整个图论建模和分析生物网络的基础。我们甚至简要讨论了用于处理图形数据结构的 R 脚本。
• 第三章：讨论图论中广泛研究的各种算法，如图遍历算法。在生物网络中，功率图分析是一种重要的图分析方法，我们将通过实例进行讨论。此外，还引入并演示了各种节点中心性度量R脚本。
• 第 4 章：现实世界的网络遵循某些特殊的拓扑属性，这使得它们不同于通常的图。因此，它们被分类为各种网络模型。我们使用不同的模型及其属性，并使用 R 包实现它们。
• 第 5 章：讨论了三个生物网络存储库的来源，它们是公开可用的数据库。本章从对流行和最近使用的数据库格式的基本介绍开始。这是生物网络相关研究的丰富篇章。
• 第 6 章：介绍了基因表达网络以及表达网络的数据生成源。整体讨论分为两个部分，in-silico network inference 和 post inference analysis。已经根据各种算法讨论了如何识别基因网络模块以及如何对表达网络中的重要基因进行排序。我们甚至讨论了各种在线和离线软件工具来进行基因表达网络推断和分析。
• 第 7 章：蛋白质及其物理相互作用网络对于在生物系统中建立真正的大分子连接至关重要。已经强调了如何通过实验产生这种相互作用并通过计算进行预测。最近，蛋白质网络比对在比较网络分析中变得越来越重要，以寻找进化上保守的蛋白质，我们将在本章中介绍。很少讨论处理功能性蛋白质复合物检测的算法。
• 第 8 章：最后，我们介绍了具有输入数据源的脑连接组网络，并介绍了脑连接组网络分析的趋势。

## 统计代写|网络分析代写Network Analysis代考|Basic concepts

$\mathcal{E}=(A, B) \$ \$(B, C),(C, D),(C, E),(E, E),(E, F),(E, D),(F, B)$ ，其中边是一对无序的节点，它们 之间有互连。图形 $\mathcal{G}$ 称为无向图。节点 $E$ 通过环边与自身相连。没有我的循环结构的图称为简单图。

$\left(v_i, v_{i+1}\right) \in \mathcal{G}(\mathcal{E})$. 但是，在有向图的情况下，有向路径将顶点序列连接起来，并增加了所有边都朝向同一方向 的限制。

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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。