Results 41 to 50 of about 3,284,626 (292)
Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series [PDF]
, 2011 To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level.
Dondelinger, F. +8 more
core +1 more source
Avoiding spurious feedback loops in the reconstruction of gene regulatory networks with dynamic bayesian networks [PDF]
, 2009 Feedback loops and recurrent structures are essential to the regulation and stable control of complex biological systems. The application of dynamic as opposed to static Bayesian networks is promising in that, in principle, these feedback loops can be ...
Husmeier, D. +3 more
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Journal of Industrial Engineering and Management, 2016 Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain.
Faycal Mimouni, Abdellah Abouabdellah
doaj +1 more source
Redai dili, 2023 Owing to the effects of rural tourism and urbanization, the frequent participation of external market activities in traditional villages has increased the sensitivity and fragility of villages.
Chu Jinlong +3 more
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Monotonicity in Bayesian Networks
CoRR, 2004 Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
van der Gaag, L.C. +2 more
openaire +6 more sources
Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
Bayesian optimization on networks
Journal of Computational PhysicsThis paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points.
W. Li, D. Sanz-Alonso, R. Yang
openaire +4 more sources
Modeling dynamic reliability using dynamic Bayesian networks [PDF]
, 2006 This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this
Noyes, Daniel, Tchangani, Ayeley
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FEBS Letters, EarlyView.Which brain hemisphere controls writing? This review synthesizes all five functional transcranial Doppler studies of writing (handwriting and typing) published to date in adults and children. Right‐handers show left‐hemispheric dominance for the linguistic component of writing (written word generation), whereas findings for left‐handers remain ...
Marietta Papadatou‐Pastou
wiley +1 more source
Layer wise Scaled Gaussian Priors for Markov Chain Monte Carlo Sampled deep Bayesian neural networks
Frontiers in Artificial IntelligencePrevious work has demonstrated that initialization is very important for both fitting a neural network by gradient descent methods, as well as for Variational inference of Bayesian neural networks.
Devesh Jawla, John Kelleher
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