Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

Jacques Demongeot; Mariem Jelassi; Hana Hazgui; Slimane Ben Miled; Narjes Bellamine Ben Saoud; Carla Taramasco

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Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

机译：生物网络熵：神经记忆网络，遗传调控网络和社会流行网络的例子

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Networks used in biological applications at different scales (molecule, cell and population) are of different types: neuronal, genetic, and social, but they share the same dynamical concepts, in their continuous differential versions (e.g., non-linear Wilson-Cowan system) as well as in their discrete Boolean versions (e.g., non-linear Hopfield system); in both cases, the notion of interaction graph G(J) associated to its Jacobian matrix J , and also the concepts of frustrated nodes, positive or negative circuits of G(J) , kinetic energy, entropy, attractors, structural stability, etc., are relevant and useful for studying the dynamics and the robustness of these systems. We will give some general results available for both continuous and discrete biological networks, and then study some specific applications of three new notions of entropy: (i) attractor entropy, (ii) isochronal entropy and (iii) entropy centrality; in three domains: a neural network involved in the memory evocation, a genetic network responsible of the iron control and a social network accounting for the obesity spread in high school environment.

机译：在不同规模（分子，细胞和种群）的生物应用中使用的网络具有不同的类型：神经元，遗传和社会网络，但它们在连续的差分版本（例如非线性Wilson-Cowan系统）中共享相同的动力学概念。）以及它们的离散布尔版本（例如，非线性Hopfield系统）;在这两种情况下，都与它的雅可比矩阵J相关联的相互作用图G（J）的概念，以及受挫节点，G（J）的正负电路，动能，熵，吸引子，结构稳定性等概念。，对于研究这些系统的动态性和鲁棒性至关重要。我们将给出一些可用于连续和离散生物网络的一般结果，然后研究三种新的熵概念的一些特定应用：（i）吸引熵，（ii）等时熵和（iii）熵中心性;在三个领域中：一个涉及记忆记忆的神经网络，一个负责铁控制的遗传网络和一个解释肥胖在高中环境中传播的社交网络。

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《Entropy》 |2018年第1期|共21页
作者
Jacques Demongeot; Mariem Jelassi; Hana Hazgui; Slimane Ben Miled; Narjes Bellamine Ben Saoud; Carla Taramasco;
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中图分类生理学;
关键词
biological networksdynamic entropyisochronal entropyattractor entropyentropy centralityrobustness;

机译：生物网络动态熵等时熵吸引子熵中心性鲁棒性;

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机译：新的Markov-Shannon熵模型可评估复杂网络中的连接质量：从分子到细胞途径，寄生虫宿主，神经网络，工业网络和法律社会网络。
2. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX) [J] . Wunsch Andreas, Liesch Tanja, Broda Stefan Hydrology and Earth System Sciences Discussions . 2021,第3期

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6. Biological Networks Entropies: Examples in Neural Memory Networks Genetic Regulation Networks and Social Epidemic Networks [O] . Jacques Demongeot, Mariem Jelassi, Hana Hazgui, 2018

机译：生物网络熵：神经记忆网络遗传调节网络和社会流行网络中的示例
7. Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks [O] . Jacques Demongeot, Mariem Jelassi, Hana Hazgui, 2018

机译：生物网络熵：神经记忆网络，遗传调节网络和社会流行网络中的示例
8. Hierarchical Neural Network Based Data Processing System for Ground- Penetrating Radar. An End of Year Report for CH/1049/6: Application of Neural Networks Coupled With Genetic Algorithms to Optimize Soil Cleanup Operations in Cold Climates [R] . Sullivan, J. M. 1997

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Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks

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