更改

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.

基因共同表达网络可以解释为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。

基因共同表达网络可以解释为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.

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 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.

===Metabolic networks===

===Metabolic networks===

 

{{main article|metabolic network}}

{{main article|metabolic network}}

The chemical compounds of a living cell are connected by biochemical reactions which convert one compound into another. The reactions are catalyzed by enzymes. Thus, all compounds in a cell are parts of an intricate biochemical network of reactions which is called metabolic network. It is possible to use network analyses to infer how selection acts on metabolic pathways.

The chemical compounds of a living cell are connected by biochemical reactions which convert one compound into another. The reactions are catalyzed by enzymes. Thus, all compounds in a cell are parts of an intricate biochemical network of reactions which is called metabolic network. It is possible to use network analyses to infer how selection acts on metabolic pathways.

===Signaling networks===

===Signaling networks===

 

{{main article|Cell signaling}}

{{main article|Cell signaling}}

Signals are transduced within cells or in between cells and thus form complex signaling networks. For instance, in the MAPK/ERK pathway is transduced from the cell surface to the cell nucleus by a series of protein–protein interactions, phosphorylation reactions, and other events. Signaling networks typically integrate protein–protein interaction networks, gene regulatory networks, and metabolic networks.

Signals are transduced within cells or in between cells and thus form complex signaling networks. For instance, in the MAPK/ERK pathway is transduced from the cell surface to the cell nucleus by a series of protein–protein interactions, phosphorylation reactions, and other events. Signaling networks typically integrate protein–protein interaction networks, gene regulatory networks, and metabolic networks.

信号在细胞内或细胞之间传递，从而形成复杂的信号网络。例如，MAPK/ERK 通路通过一系列蛋白质-蛋白质相互作用、磷酸化反应和其他事件从细胞表面传递到细胞核。信号网络通常整合蛋白质-蛋白质相互作用网络、基因调控网络和代谢网络。

信号在细胞内或细胞之间传递，从而形成复杂的信号网络。例如，MAPK/ERK 通路通过一系列蛋白质-蛋白质相互作用、磷酸化反应和其他事件将信号从细胞表面传递到细胞核。信号网络通常包含了蛋白质-蛋白质相互作用网络、基因调控网络和代谢网络。

===Neuronal networks===

===Neuronal networks===

 

{{main article| Biological neural network}}

{{main article| Biological neural network}}

The complex interactions in the brain make it a perfect candidate to apply network theory. Neurons in the brain are deeply connected with one another and this results in complex networks being present in the structural and functional aspects of the brain. For instance, small-world network properties have been demonstrated in connections between cortical areas of the primate brain or during swallowing in humans. This suggests that cortical areas of the brain are not directly interacting with each other, but most areas can be reached from all others through only a few interactions.

The complex interactions in the brain make it a perfect candidate to apply network theory. Neurons in the brain are deeply connected with one another and this results in complex networks being present in the structural and functional aspects of the brain. For instance, small-world network properties have been demonstrated in connections between cortical areas of the primate brain or during swallowing in humans. This suggests that cortical areas of the brain are not directly interacting with each other, but most areas can be reached from all others through only a few interactions.

 

 

===Food webs===

===Food webs===

 

{{main article|Food web}}

{{main article|Food web}}

All organisms are connected to each other through feeding interactions. That is, if a species eats or is eaten by another species, they are connected in an intricate food web of predator and prey interactions. The stability of these interactions has been a long-standing question in ecology. That is to say, if certain individuals are removed, what happens to the network (i.e. does it collapse or adapt)? Network analysis can be used to explore food web stability and determine if certain network properties result in more stable networks. Moreover, network analysis can be used to determine how selective removals of species will influence the food web as a whole. This is especially important considering the potential species loss due to global climate change.

All organisms are connected to each other through feeding interactions. That is, if a species eats or is eaten by another species, they are connected in an intricate food web of predator and prey interactions. The stability of these interactions has been a long-standing question in ecology. That is to say, if certain individuals are removed, what happens to the network (i.e. does it collapse or adapt)? Network analysis can be used to explore food web stability and determine if certain network properties result in more stable networks. Moreover, network analysis can be used to determine how selective removals of species will influence the food web as a whole. This is especially important considering the potential species loss due to global climate change.

所有的生物都是通过食物的相互作用联系在一起的。也就是说，如果一个物种吃了另一个物种或被另一个物种吃了，它们就在一个复杂的捕食者和猎物相互作用的食物网中联系起来。这些相互作用的稳定性一直是生态学中的一个长期问题。也就是说，如果某些个体被移除，那么网络(即。它是崩溃还是适应) ？网络分析可以用来探索食物网的稳定性，并确定某些网络特性是否会导致更稳定的网络。此外，网络分析可以用来确定物种的选择性迁移将如何影响整个食物网。考虑到全球气候变化可能造成的物种损失，这一点尤其重要。

所有的生物都具有捕食别的物种或被别的物种捕食的情况。也就是说，如果一个物种吃了另一个物种或被另一个物种吃了，它们就在一个复杂的捕食者和被捕食者相互作用的食物网中被联系起来。这些相互作用的稳定性长期困扰着生态学家们。具体来说，如果某些个体被移除，那么网络会怎么样？(即它是崩溃还是重新适应) ？网络分析可以用来探索食物网的稳定性，并确定某些网络特性是否会导致更稳定的网络。此外，网络分析可以用来确定物种的选择性迁移将如何影响整个食物网。考虑到全球气候变化可能导致大量物种的消失，使用网络分析来研究食物网的特性是尤其重要的。

===Between-species interaction networks===

===Between-species interaction networks===

 

{{main article|social relation}}

{{main article|social relation}}

In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science, it has become possible to scale up pairwise interactions to include individuals of many species involved in many sets of interactions to understand the structure and function of larger ecological networks. The use of network analysis can allow for both the discovery and understanding how these complex interactions link together within the system’s network, a property which has previously been overlooked. This powerful tool allows for the study of various types of interactions (from competitive to cooperative) using the same general framework. For example, plant-pollinator interactions are mutually beneficial and often involve many different species of pollinators as well as many different species of plants. These interactions are critical to plant reproduction and thus the accumulation of resources at the base of the food chain for primary consumers, yet these interaction networks are threatened by anthropogenic change. The use of network analysis can illuminate how pollination networks work and may in turn inform conservation efforts. Within pollination networks, nestedness (i.e., specialists interact with a subset of species that generalists interact with), redundancy (i.e., most plants are pollinated by many pollinators), and modularity play a large role in network stability. These network properties may actually work to slow the spread of disturbance effects through the system and potentially buffer the pollination network from anthropogenic changes somewhat. Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time. Recent research into these complex species interactions networks is highly concerned with understanding what factors (e.g., diversity) lead to network stability.

In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science, it has become possible to scale up pairwise interactions to include individuals of many species involved in many sets of interactions to understand the structure and function of larger ecological networks. The use of network analysis can allow for both the discovery and understanding how these complex interactions link together within the system’s network, a property which has previously been overlooked. This powerful tool allows for the study of various types of interactions (from competitive to cooperative) using the same general framework. For example, plant-pollinator interactions are mutually beneficial and often involve many different species of pollinators as well as many different species of plants. These interactions are critical to plant reproduction and thus the accumulation of resources at the base of the food chain for primary consumers, yet these interaction networks are threatened by anthropogenic change. The use of network analysis can illuminate how pollination networks work and may in turn inform conservation efforts. Within pollination networks, nestedness (i.e., specialists interact with a subset of species that generalists interact with), redundancy (i.e., most plants are pollinated by many pollinators), and modularity play a large role in network stability. These network properties may actually work to slow the spread of disturbance effects through the system and potentially buffer the pollination network from anthropogenic changes somewhat. Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time. Recent research into these complex species interactions networks is highly concerned with understanding what factors (e.g., diversity) lead to network stability.

在生物学中，成对相互作用历来是密集研究的焦点。随着网络科学的最新进展，已经有可能扩大成对的相互作用，以包括许多物种的个体参与多组相互作用，从而理解更大的生态网络的结构和功能。使用网络分析可以发现和理解这些复杂的交互如何在系统的网络中连接在一起，这是以前被忽视的属性。这个强大的工具允许使用相同的总体框架研究各种类型的交互(从竞争到合作)。例如，植物与传粉者之间的相互作用是互惠互利的，通常涉及许多不同种类的传粉者以及许多不同种类的植物。这些相互作用对植物生殖和初级消费者食物链底层的资源积累至关重要，然而这些相互作用网络受到人为变化的威胁。网络分析的使用可以说明授粉网络是如何工作的，反过来也可以为保护工作提供信息。在授粉网络中，内嵌性(即专家与多面手相互作用的物种子集相互作用)、冗余性(即大多数植物是由许多授粉者授粉的)和模块性在网络稳定性中扮演着重要角色。这些网络属性实际上可以减缓干扰效应通过系统的传播，并可能缓冲授粉网络的人为变化。研究人员甚至可以将物种相互作用网络的现有结构与古代网络的历史重建进行比较，以确定网络随着时间的推移是如何发生变化的。最近对这些复杂物种间相互作用网络的研究高度关注于理解什么因素(如多样性)导致网络的稳定性。

在生物学中，成对相互作用历来是研究的重点。随着近几年网络科学的发展，成对相互作用很有可能被延展，甚至涵盖有许多物种个体参与的多组相互作用，进而帮助我们理解更大生态网络的结构和功能。使用网络分析可以发现和理解这些复杂的交互是如何在系统的网络中连接在一起的，这是以前经常被忽视的一点。这个强大的工具使得同一个普世框架研究各种类型的交互(从竞争到合作)成为可能。例如，植物与传粉者之间的相互作用是互惠互利的，通常会涉及到许多不同种类的传粉者以及许多不同种类的植物。这些相互作用对植物的生殖和食物链底层初级消费者的资源积累至关重要，然而这些相互作用网络正受到人造变化的威胁。网络分析的使用可以说明授粉网络是如何工作的，反过来也可以为相关植物保护工作提供必要信息。在授粉网络中，内嵌性(某一领域的植物被少数几个昆虫授粉，剩下的各种昆虫只对少数几种植物授粉)、冗余性(即大多数植物是由许多授粉者授粉的)和模块性（一些物种相互之间连接的很紧密，与剩余的物种连接不紧密，由此形成一个个团簇，也可称之为“模块”）在网络稳定性中扮演着重要角色。这些网络属性实际上可以减缓干扰效应在系统上的传播，并可能缓冲授粉网络受到的人造变化的影响。研究人员甚至可以将物种相互作用网络的现有结构与该网络过去的结构进行比较，以确定网络在时间尺度上是如何演化的。最近对这些复杂物种间相互作用网络的研究聚焦于理解是什么因素(如多样性)造就了网络的稳定性。

===Within-species interaction networks===

===Within-species interaction networks===

 

{{main article|social relation}}

{{main article|social relation}}

Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level. One of the most attractive features of the network paradigm would be that it provides a single conceptual framework in which the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.) can be studied.

Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level. One of the most attractive features of the network paradigm would be that it provides a single conceptual framework in which the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.) can be studied.

网络分析提供了量化个体之间关联的能力，这使得在物种和/或种群水平上推断整个网络的细节成为可能。网络范式最吸引人的特征之一是，它提供了一个单一的概念框架，其中动物的社会组织在所有层次(个人，二元，群体，人口)和所有类型的互动(攻击性，合作，性等。)可以研究。

网络分析可以量化个体之间的关联，这使得在物种和/或种群水平上推断整个网络的细节成为可能。网络范式最吸引人的特征之一是，它提供了一个单一的概念框架，使得社会性动物在所有层次上(单，双，组，群)和所有类型的互动中(攻击，合作，性等等)都可以被研究。

 

Researchers interested in ethology across a multitude of taxa, from insects to primates, are starting to incorporate network analysis into their research. Researchers interested in social insects (e.g., ants and bees) have used network analyses to better understand division of labor, task allocation, and foraging optimization within colonies; Other researchers are interested in how certain network properties at the group and/or population level can explain individual level behaviors. Studies have demonstrated how animal social network structure can be influenced by factors ranging from characteristics of the environment to characteristics of the individual, such as developmental experience and personality. At the level of the individual, the patterning of social connections can be an important determinant of fitness, predicting both survival and reproductive success. At the population level, network structure can influence the patterning of ecological and evolutionary processes, such as frequency-dependent selection and disease and information transmission. For instance, a study on wire-tailed manakins (a small passerine bird)  found that a male’s degree in the network largely predicted the ability of the male to rise in the social hierarchy (i.e. eventually obtain a territory and matings). In bottlenose dolphin groups, an individual’s degree and betweenness centrality values may predict whether or not that individual will exhibit certain behaviors, like the use of side flopping and upside-down lobtailing to lead group traveling efforts; individuals with high betweenness values are more connected and can obtain more information, and thus are better suited to lead group travel and therefore tend to exhibit these signaling behaviors more than other group members.  

Researchers interested in ethology across a multitude of taxa, from insects to primates, are starting to incorporate network analysis into their research. Researchers interested in social insects (e.g., ants and bees) have used network analyses to better understand division of labor, task allocation, and foraging optimization within colonies; Other researchers are interested in how certain network properties at the group and/or population level can explain individual level behaviors. Studies have demonstrated how animal social network structure can be influenced by factors ranging from characteristics of the environment to characteristics of the individual, such as developmental experience and personality. At the level of the individual, the patterning of social connections can be an important determinant of fitness, predicting both survival and reproductive success. At the population level, network structure can influence the patterning of ecological and evolutionary processes, such as frequency-dependent selection and disease and information transmission. For instance, a study on wire-tailed manakins (a small passerine bird)  found that a male’s degree in the network largely predicted the ability of the male to rise in the social hierarchy (i.e. eventually obtain a territory and matings). In bottlenose dolphin groups, an individual’s degree and betweenness centrality values may predict whether or not that individual will exhibit certain behaviors, like the use of side flopping and upside-down lobtailing to lead group traveling efforts; individuals with high betweenness values are more connected and can obtain more information, and thus are better suited to lead group travel and therefore tend to exhibit these signaling behaviors more than other group members.  

从昆虫到灵长类动物，研究人员对动物行为学感兴趣，并开始将网络分析纳入他们的研究中。对社会性昆虫(如蚂蚁和蜜蜂)感兴趣的研究人员利用网络分析来更好地理解群体内的分工、任务分配和觅食优化; 其他研究人员则对群体和/或种群水平的某些网络特性如何解释个体水平的行为感兴趣。研究表明，动物的社会网络结构是如何受到从环境特征到个体特征，如发展经验和人格等因素的影响。在个体层面上，社会联系的模式可能是适应性的一个重要决定因素，预测生存和繁殖的成功。在种群水平上，网络结构可以影响生态和进化过程的模式，如频率依赖性选择和疾病及信息传递。例如，一项针对线尾 manakins (一种小型雀形目鸟)的研究发现，雄性网络中的学位在很大程度上预测了雄性在社会等级中上升的能力(即,。最终获得领地和交配)。在宽吻海豚群体中，个体的程度和中介中心性价值观可以预测个体是否会表现出某些行为，比如使用侧翻和倒挂龙虾来领导团队旅行; 中介价值观较高的个体相互联系更多，可以获得更多信息，因此更适合领导团队旅行，因此比其他团队成员更容易表现出这些信号传递行为。

从昆虫到灵长类动物，对动物行为学感兴趣的研究人员，开始将网络分析纳入到他们的研究中。对社会性昆虫(如蚂蚁和蜜蜂)感兴趣的研究人员利用网络分析来更好地理解，群体内的昆虫的分工、任务分配和觅食优化; 其他研究人员感兴趣的点则在群体和/或种群水平的某些网络特性如何解释个体水平的行为上。研究表明，动物的社会网络结构是如何受从环境特征到个体特征，如经验发展和人格等因素的影响的。在个体层面上，社会联系的模式可能预测着个体生存和繁殖的成功，是个体适应性的一个关键决定因素。在种群水平上，网络结构可以影响生态和进化过程的模式，如频率依赖性选择和疾病及信息的传递。例如，一项针对线尾 manakins (一种小型雀形目鸟)的研究发现，在网络中雄性个体的度值在很大程度上预测了雄性在社会等级中进阶的能力(即最终可获得领地和交配机会)。在宽吻海豚群体中，个体的度值和中介中心性值可以预测个体是否会表现出某些行为，比如使用侧翻和头潜入水中尾巴举向空中来领导团队的迁移; 中介值观较高的个体有着更多的连接，可以获得更多信息，因此更适合领导团队迁行，因此比其他团队成员更容易表现出传递这些信号的行为。

Social network analysis can also be used to describe the social organization within a species more generally, which frequently reveals important proximate mechanisms promoting the use of certain behavioral strategies. These descriptions are frequently linked to ecological properties (e.g., resource distribution). For example, network analyses revealed subtle differences in the group dynamics of two related equid fission-fusion species, Grevy’s zebra and onagers, living in variable environments; Grevy’s zebras show distinct preferences in their association choices when they fission into smaller groups, whereas onagers do not. Similarly, researchers interested in primates have also utilized network analyses to compare social organizations across the diverse primate order, suggesting that using network measures (such as centrality, assortativity, modularity, and betweenness) may be useful in terms of explaining the types of social behaviors we see within certain groups and not others.  

Social network analysis can also be used to describe the social organization within a species more generally, which frequently reveals important proximate mechanisms promoting the use of certain behavioral strategies. These descriptions are frequently linked to ecological properties (e.g., resource distribution). For example, network analyses revealed subtle differences in the group dynamics of two related equid fission-fusion species, Grevy’s zebra and onagers, living in variable environments; Grevy’s zebras show distinct preferences in their association choices when they fission into smaller groups, whereas onagers do not. Similarly, researchers interested in primates have also utilized network analyses to compare social organizations across the diverse primate order, suggesting that using network measures (such as centrality, assortativity, modularity, and betweenness) may be useful in terms of explaining the types of social behaviors we see within certain groups and not others.  

社会网络分析也可以用来更广泛地描述一个物种内的社会组织，它经常揭示促进某些行为策略的使用的重要的近似机制。这些描述经常与生态属性(例如，资源分配)联系在一起。例如，网络分析揭示了生活在变化环境中的两个相关的等分裂融合物种——格雷维斑马和骑驴——的群体动力学的细微差异; 格雷维斑马在分裂为较小的群体时，在关联选择上表现出明显的偏好，而骑驴则不然。同样，对灵长类动物感兴趣的研究人员也利用网络分析来比较不同灵长类动物的社会组织，这表明使用网络测量(如集中性、协调性、模块性和介于性)可能有助于解释我们在某些群体中看到的社会行为类型，而不是其他群体。

社会网络分析也可以用来更广泛地描述一个物种内的社会组织，它经常揭示出那些促进某些行为策略使用的重要邻近机制。这些描述经常与生态属性(例如，资源分配)联系在一起。例如，网络分析揭示了生活在变化环境中的两个相关的等裂变融合物种——格雷维斑马和骑驴——的群体动力学的细微差异（裂变融合社会是这样一个社会，其规模和组成随着时间的流逝以及动物在整个环境中的移动而变化；动物会合并成一团（融合）-例如在一个地方睡觉-或分裂（裂变）-例如白天在小组中觅食）; 格雷维斑马在分裂为较小的群体时，在关联选择上表现出明显的偏好，而骑驴则不然。同样，对灵长类动物感兴趣的研究人员也利用网络分析来比较不同灵长类动物的社会组织，这表明以网络的视角进行测量(如集中性、协调性、模块性和中间性)可能有助于解释我们在某些特定群体中看到的社会行为类型。

Finally, social network analysis can also reveal important fluctuations in animal behaviors across changing environments. For example, network analyses in female chacma baboons (Papio hamadryas ursinus) revealed important dynamic changes across seasons which were previously unknown; instead of creating stable, long-lasting social bonds with friends, baboons were found to exhibit more variable relationships which were dependent on short-term contingencies related to group level dynamics as well as environmental variability. Changes in an individual's social network environment can also influence characteristics such as 'personality': for example, social spiders that huddle with bolder neighbours tend to increase also in boldness. This is a very small set of broad examples of how researchers can use network analysis to study animal behavior. Research in this area is currently expanding very rapidly, especially since the broader development of animal borne tags and computer vision that can be used to automate the collection of social associations. Social network analysis is a valuable tool for studying animal behavior across all animal species, and has the potential to uncover new information about animal behavior and social ecology that was previously poorly understood.

Finally, social network analysis can also reveal important fluctuations in animal behaviors across changing environments. For example, network analyses in female chacma baboons (Papio hamadryas ursinus) revealed important dynamic changes across seasons which were previously unknown; instead of creating stable, long-lasting social bonds with friends, baboons were found to exhibit more variable relationships which were dependent on short-term contingencies related to group level dynamics as well as environmental variability. Changes in an individual's social network environment can also influence characteristics such as 'personality': for example, social spiders that huddle with bolder neighbours tend to increase also in boldness. This is a very small set of broad examples of how researchers can use network analysis to study animal behavior. Research in this area is currently expanding very rapidly, especially since the broader development of animal borne tags and computer vision that can be used to automate the collection of social associations. Social network analysis is a valuable tool for studying animal behavior across all animal species, and has the potential to uncover new information about animal behavior and social ecology that was previously poorly understood.

最后，社会网络分析还可以揭示动物行为在不断变化的环境中的重要波动。例如，对雌性沙卡马狒狒(Papio hamadryas ursinus)的网络分析揭示了以前未知的跨季节的重要动态变化; 结果发现，狒狒与朋友之间没有建立稳定、持久的社会联系，而是表现出更多的变化关系，这些关系依赖于与群体层面动态以及环境变化有关的短期偶然事件。个体社交网络环境的变化也会影响个体的性格特征，例如，与胆大的邻居挤在一起的社交蜘蛛也会更加大胆。这只是研究人员如何利用网络分析来研究动物行为的一小部分广泛的例子。这一领域的研究目前正在迅速扩展，特别是随着动物身上的标签和计算机视觉技术的广泛发展，这些技术可以用来自动收集社会关系。社会网络分析是研究所有动物物种的动物行为的一个有价值的工具，并且有可能发现以前鲜为人知的关于动物行为和社会生态的新信息。

最后，社会网络分析还可以揭示动物行为在不断变化的环境中的关键波动。例如，对雌性沙卡马狒狒(Papio hamadryas ursinus)的网络分析揭示了以前未知的跨季节的重要动态变化; 结果发现，狒狒与朋友之间没有建立稳定、持久的社会联系，相反，它们之间的关系是多变的，这些关系依赖于在动态群体层面的短期偶然事件以及环境的变化。个体社交网络环境的变化也会影响个体的性格特征，例如，与胆大的邻居挤在一起的蜘蛛也会更加大胆。这只是研究人员利用网络分析来研究动物行为的一小部分。这一领域的研究目前正在迅速扩展，特别是随着动物身体标记技术和计算机视觉技术的广泛发展，这些技术可以用来自动收集个体的社会关系。社会网络分析是研究所有物种的行为的一个有价值的工具，并且有可能发现以前鲜为人知的关于动物行为和社会生态的新信息。

* [[List of omics topics in biology]]

* [[List of omics topics in biology]]

 

* [[Biological network inference]]

* [[Biological network inference]]

 

* [[Applied Statistics]]

* [[Applied Statistics]]

 

* [[Biostatistics]]

* [[Biostatistics]]

 

* [[Computational Biology]]

* [[Computational Biology]]

 

* [[Systems biology]]

* [[Systems biology]]

 

* [[Weighted correlation network analysis]]  

* [[Weighted correlation network analysis]]  

 

* [[Interactome]]

* [[Interactome]]

 

* [[Network medicine]]

* [[Network medicine]]