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Frontiers in Genetics, 2022 COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the disease can spread throughout the body and damage many other tissues, leading to multiorgan failure in severe cases. The highly variable symptom severity is
Yue Hu +13 more
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Inference of a Boolean Network From Causal Logic Implications
Frontiers in Genetics, 2022 Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model ...
Parul Maheshwari +3 more
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Quantifying the ‘end of history’ through a Bayesian Markov-chain approach
Royal Society Open Science, 2022 Political regimes have been changing throughout human history. After the apparent triumph of liberal democracies at the end of the twentieth century, Francis Fukuyama and others have been arguing that humankind is approaching an ‘end of history’ (EoH) in
Florian Klimm
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Entropy, 2016 Complex network methodology is very useful for complex system exploration. However, the relationships among variables in complex systems are usually not clear.
Yanzhu Hu, Huiyang Zhao, Xinbo Ai
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Control Meets Inference: Using Network Control to Uncover the Behaviour of Opponents
Entropy, 2022 Using observational data to infer the coupling structure or parameters in dynamical systems is important in many real-world applications. In this paper, we propose a framework of strategically influencing a dynamical process that generates observations ...
Zhongqi Cai +2 more
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Frontiers in Applied Mathematics and Statistics, 2022 In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors that determine the overall function of a network of neurons.
Thierry Nieus +5 more
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Decoupling approximation robustly reconstructs directed dynamical networks
New Journal of Physics, 2018 Methods for reconstructing the topology of complex networks from time-resolved observations of node dynamics are gaining relevance across scientific disciplines.
Nikola Simidjievski +5 more
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Granger-Causality Inference of the Existence of Unobserved Important Components in Network Analysis
Entropy, 2021 Detecting causal interrelationships in multivariate systems, in terms of the Granger-causality concept, is of major interest for applications in many fields. Analyzing all the relevant components of a system is almost impossible, which contrasts with the
Heba Elsegai
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Network inference with hidden units
Mathematical Biosciences and Engineering, 2014 We derive learning rules for finding the connections between units in stochastic dynamical networks from the recorded history of a ``visible'' subset of the units. We consider two models. In both of them, the visible units are binary and stochastic. In one model the ``hidden'' units are continuous-valued, with sigmoidal activation functions, and in the
Tyrcha, Joanna, Hertz, John
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Network Inference From Co-Occurrences [PDF]
IEEE Transactions on Information Theory, 2008 The recovery of network structure from experimental data is a basic and fundamental problem. Unfortunately, experimental data often do not directly reveal structure due to inherent limitations such as imprecision in timing or other observation mechanisms.
Rabbat, Michael +2 more
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