Automated reverse engineering of nonlinear dynamical systems
about
Reverse engineering and identification in systems biology: strategies, perspectives and challengesInferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regenerationDynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersionData-based reconstruction of complex geospatial networks, nodal positioning and detection of hidden nodesExploiting genomic knowledge in optimising molecular breeding programmes: algorithms from evolutionary computingPhilosophy of science. Machine scienceForecasting synchronizability of complex networks from data.Generating self-organizing collective behavior using separation dynamics from experimental data.Chaos as an intermittently forced linear systemDiscovering governing equations from data by sparse identification of nonlinear dynamical systems.Benchmarks for identification of ordinary differential equations from time series data.Reconstructing generalized logical networks of transcriptional regulation in mouse brain from temporal gene expression dataReconstructing nonlinear dynamic models of gene regulation using stochastic sampling.Towards monitoring real-time cellular response using an integrated microfluidics-matrix assisted laser desorption ionisation/nanoelectrospray ionisation-ion mobility-mass spectrometry platform.Reconstructing propagation networks with natural diversity and identifying hidden sources.Automated refinement and inference of analytical models for metabolic networksDiscrete dynamical system modelling for gene regulatory networks of 5-hydroxymethylfurfural tolerance for ethanologenic yeastSystems cell biology.Functional inference of complex anatomical tendinous networks at a macroscopic scale via sparse experimentationFinding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it.Computational Models for Neuromuscular FunctionEvaluating a common semi-mechanistic mathematical model of gene-regulatory networks.Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.Universal data-based method for reconstructing complex networks with binary-state dynamics.Data-driven discovery of partial differential equations.Systematic identification of signal-activated stochastic gene regulation.Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso InitializationA linear-encoding model explains the variability of the target morphology in regeneration.Uncovering hidden nodes in complex networks in the presence of noise.Reconstruction of normal forms by learning informed observation geometries from data.Prediction of dynamical systems by symbolic regression.Inferring and quantifying the role of an intrinsic current in a mechanism for a half-center bursting oscillation: A dominant scale and hybrid dynamical systems analysis.Scientific discovery as a combinatorial optimisation problem: how best to navigate the landscape of possible experiments?Serotonergic regulation of melanocyte conversion: A bioelectrically regulated network for stochastic all-or-none hyperpigmentation.ODEion--a software module for structural identification of ordinary differential equations.Reconstructing complex networks without time series.Sparse model selection via integral terms.Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.The neuronal replicator hypothesis.Abductive learning of quantized stochastic processes with probabilistic finite automata.
P2860
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P2860
Automated reverse engineering of nonlinear dynamical systems
description
2007 nî lūn-bûn
@nan
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
2007年论文
@zh
2007年论文
@zh-cn
name
Automated reverse engineering of nonlinear dynamical systems
@ast
Automated reverse engineering of nonlinear dynamical systems
@en
type
label
Automated reverse engineering of nonlinear dynamical systems
@ast
Automated reverse engineering of nonlinear dynamical systems
@en
prefLabel
Automated reverse engineering of nonlinear dynamical systems
@ast
Automated reverse engineering of nonlinear dynamical systems
@en
P2860
P356
P1476
Automated reverse engineering of nonlinear dynamical systems
@en
P2093
Hod Lipson
Josh Bongard
P2860
P304
P356
10.1073/PNAS.0609476104
P407
P577
2007-06-06T00:00:00Z