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We define the non-stationary DBN model, present an MCMC sampling algorithm for learning the structure of the model from time-series data under different assumptions, and demonstrate the effectiveness of the algorithm on both simulated and biological data.
The proposed PCA-Gabor decomposition is compared with other time-frequency data reduction methods such as the time-frequency PCA approach alone and standard matching pursuit methods using a Gabor dictionary for both simulated and biological data.
Experiments on both simulated and biological data are designed to investigate the properties of the proposed method and existing methods.
We test our methods for both simulated and biological data on a 3192 MHz Intel Xeon workstation.
Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity.
We benchmark our algorithms to both simulated and biological datasets and demonstrate the dramatic improvements in runtime at a range of dataset sizes.
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We demonstrate that our modeling framework is able to accurately capture the majority of the known interactions in both the simulated and biological data.
We validated the method both on simulated and biological data.
MetaCluster 5.0 outperforms existing binning algorithms for both simulated and real biological datasets.
We analyze the performance of our parsimony-based approach to ancestral network reconstruction on both simulated and real biological data.
To assess the performance of PSIKO with regard to Q-matrix estimation and inference of founder number, we rigorously tested it on both simulated and real biological datasets.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com