Bayesian networks in neuroscience: a survey.
Bayesian networks in neuroscience: a survey.
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DOI：
10.3389/fncom.2014.00131
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发表时间：
2014
影响因子：
3.2
通讯作者：
Larrañaga P
中科院分区：
文献类型：
--
作者：
Bielza C;Larrañaga P
贝叶斯网络是一种位于统计学和机器学习交叉点的概率图模型。它们已被证明是强大的工具来编码依赖关系的不确定性下的域的变量之间。由于其通用性，贝叶斯网络可以适应连续和离散变量，以及时间过程。在本文中，我们回顾贝叶斯网络，以及它们如何通过结构学习算法从数据中自动学习。此外，我们研究了用户如何利用这些网络进行推理的精确或近似推理算法，通过图形结构传播给定的证据。尽管它们在许多领域都有应用，但它们在神经科学中的应用很少，它们专注于特定问题，例如来自神经成像数据的功能连接分析。在这里，我们调查了神经科学中的关键研究，贝叶斯网络被用于不同的目的：发现变量之间的关联，对模型进行概率推理，以及在有监督和无监督的情况下对新观察进行分类。该网络是从任何类型的形态学，电生理学，组学和神经成像的数据，从而扩大范围的分子，细胞，结构，功能，认知和医学的大脑方面进行研究。
Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind–morphological, electrophysiological, -omics and neuroimaging–, thereby broadening the scope–molecular, cellular, structural, functional, cognitive and medical– of the brain aspects to be studied.
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影响因子：
6
作者：
Chickering, DM
通讯作者：
Chickering, DM
影响因子：
16.6
作者：
Bielza, Concha;Larranaga, Pedro
通讯作者：
Larranaga, Pedro
影响因子：
16.2
作者：
Belgard TG;Marques AC;Oliver PL;Abaan HO;Sirey TM;Hoerder-Suabedissen A;García-Moreno F;Molnár Z;Margulies EH;Ponting CP
通讯作者：
Ponting CP
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
影响因子：
4.8
作者：
Burge, John;Lane, Terran;Clark, Vincent P.
通讯作者：
Clark, Vincent P.