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Complex biological systems may be represented and analyzed as computable networks. For example, ecosystems can be modeled as networks of interacting species or a protein can be modeled as a network of amino acids. Breaking a protein down farther, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the basic components of a network. Nodes represent units in the network, while edges represent the interactions between the units. Nodes can represent a wide array of biological units, from individual organisms to individual neurons in the brain. Two important properties of a network are degree and betweenness centrality. Degree (or connectivity, a distinct usage from that used in graph theory) is the number of edges that connect a node, while betweenness is a measure of how central a node is in a network. Nodes with high betweenness essentially serve as bridges between different portions of  the network (i.e. interactions must pass through this node to reach other portions of the network). In social networks, nodes with high degree or high betweenness may play important roles in the overall composition of a network.

Complex biological systems may be represented and analyzed as computable networks. For example, ecosystems can be modeled as networks of interacting species or a protein can be modeled as a network of amino acids. Breaking a protein down farther, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the basic components of a network. Nodes represent units in the network, while edges represent the interactions between the units. Nodes can represent a wide array of biological units, from individual organisms to individual neurons in the brain. Two important properties of a network are degree and betweenness centrality. Degree (or connectivity, a distinct usage from that used in graph theory) is the number of edges that connect a node, while betweenness is a measure of how central a node is in a network. Nodes with high betweenness essentially serve as bridges between different portions of  the network (i.e. interactions must pass through this node to reach other portions of the network). In social networks, nodes with high degree or high betweenness may play important roles in the overall composition of a network.

复杂的生物系统能用可计算的网络来表示和分析。例如，生态系统可以被模拟为相互作用的物种网络，一个蛋白质可以被模拟为氨基酸网络。进一步分解蛋白质，氨基酸可以表示为一个由相互连接的原子构成的网络，如碳、氮和氧。'''<font color="#ff8000"> 节点 Nodes</font>'''和'''<font color="#ff8000">边 Edges</font>'''是网络的基本组成部分。节点表示网络中的单元，边表示单元之间的相互作用。节点可以代表一系列广泛的生物单元，从单个的生物体到大脑中单个的神经元。网络的两个重要性质是'''<font color="#ff8000">度 Degree</font>'''和'''<font color="#ff8000"> 介数中心性 Betweenness Centrality</font>'''。度(或连通性，一个不同于'''<font color="#ff8000">图论 Graph Theory</font>'''的用法)是连接一个节点的边的数量，而介数中心性是衡量一个节点在网络中有多么靠近中心位置。具有高介数的节点本质上充当网络不同部分之间的桥梁(即网络其他部分的交互，必须通过这个节点)。在社会网络中，具有较高度值和较高介数的节点可能在网络的整体组成中发挥重要作用。

复杂的生物系统能用可计算的网络来表示和分析。例如，生态系统可以被模拟为相互作用的物种网络，一个蛋白质可以被模拟为氨基酸网络。进一步分解蛋白质，氨基酸可以表示为一个由相互连接的原子构成的网络，如碳、氮和氧。'''<font color="#ff8000"> 顶点 Nodes</font>'''和'''<font color="#ff8000">边 Edges</font>'''是网络的基本组成部分。顶点表示网络中的单元，边表示单元之间的相互作用。顶点可以代表一系列广泛的生物单元，从单个的生物体到大脑中单个的神经元。网络的两个重要性质是'''<font color="#ff8000">度 Degree</font>'''和'''<font color="#ff8000"> 介数中心性 Betweenness Centrality</font>'''。度(或连通性，一个不同于'''<font color="#ff8000">图论 Graph Theory</font>'''的用法)是连接一个顶点的边的数量，而介数中心性是衡量一个顶点在网络中有多么靠近中心位置。具有高介数的顶点本质上充当网络不同部分之间的桥梁(即网络其他部分的交互，必须通过这个节点)。在社会网络中，具有较高度值和较高介数的节点可能在网络的整体组成中发挥重要作用。

Many protein–protein interactions (PPIs) in a cell form protein interaction networks (PINs) where proteins are nodes and their interactions are edges. PINs are the most intensely analyzed networks in biology. There are dozens of PPI detection methods to identify such interactions. The yeast two-hybrid system is a commonly used experimental technique for the study of binary interactions.

Many protein–protein interactions (PPIs) in a cell form protein interaction networks (PINs) where proteins are nodes and their interactions are edges. PINs are the most intensely analyzed networks in biology. There are dozens of PPI detection methods to identify such interactions. The yeast two-hybrid system is a commonly used experimental technique for the study of binary interactions.

在细胞中，大量'''<font color="#ff8000">蛋白质之间相互作用 Protein-Protein Interactions(PPIs)</font>'''形成'''<font color="#ff8000">蛋白质相互作用网络 Protein Interaction Networks(PINs)</font>'''，其中蛋白质是节点，它们的相互作用是连边。蛋白质互作网络是生物学中分析最深入的网络，现有几十种基于PPIs的检测方法来识别蛋白质间的相互作用。'''<font color="#ff8000">酵母双杂交系统 Yeast Two-Hybrid System</font>'''是一种研究二元相互作用的常用实验技术。

在细胞中，大量'''<font color="#ff8000">蛋白质之间相互作用 Protein-Protein Interactions(PPIs)</font>'''形成'''<font color="#ff8000">蛋白质相互作用网络 Protein Interaction Networks(PINs)</font>'''，其中蛋白质是顶点，它们的相互作用是连边。蛋白质互作网络是生物学中分析最深入的网络，现有几十种基于PPIs的检测方法来识别蛋白质间的相互作用。'''<font color="#ff8000">酵母双杂交系统 Yeast Two-Hybrid System</font>'''是一种研究二元相互作用的常用实验技术。

Gene co-expression networks can be interpreted as association networks between variables that measure transcript abundances. These networks have been used to provide a systems biologic analysis of DNA microarray data, RNA-seq data, miRNA data etc.

Gene co-expression networks can be interpreted as association networks between variables that measure transcript abundances. These networks have been used to provide a systems biologic analysis of DNA microarray data, RNA-seq data, miRNA data etc.

'''<font color="#ff8000">基因共同表达网络 Gene Co-expression Networks </font>'''可以看作是衡量转录丰度的变量之间的关联网络。这些网络已经被用于对DNA微阵列数据、 RNA-seq数据、 miRNA等数据进行系统生物学分析。

'''<font color="#ff8000">基因共表达网络 Gene Co-expression Networks </font>'''可以看作是衡量转录丰度的变量之间的关联网络。这些网络已经被用于对DNA微阵列数据、 RNA-seq数据、 miRNA等数据进行系统生物学分析。

[[Weighted correlation network analysis|weighted gene co-expression network analysis]] is widely used to identify co-expression modules and intramodular hub genes. Co-expression modules may correspond to cell types or pathways. Highly connected intramodular hubs can be interpreted as representatives of their respective module.

[[Weighted correlation network analysis|weighted gene co-expression network analysis]] is widely used to identify co-expression modules and intramodular hub genes. Co-expression modules may correspond to cell types or pathways. Highly connected intramodular hubs can be interpreted as representatives of their respective module.