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In model-based analysis, flow event subsets are clustered by a data-driven algorithm and used to construct a generative probability model.
Here we introduce a novel generative probability model (PTMClust) that addresses the aforementioned problems encountered when using blind PTM search engines.
By accounting for combinatorial interactions between hidden variables that play a role in the protein modification process, our generative probability model aims to describe how each PTM observation is generated.
Association between the motion symbols and the words are represented by a generative model.
These local features are then clustered by a generative Gaussian mixture model (GMM) to incorporate high order statistics.
The projections are soundtracked by a generative soundscape created by Max Eastley and Henrik Ekeus.
The brain minimizes a free energy of sensory inputs defined by a generative model.
The brain minimises the free energy of sensory inputs defined by a generative model.
The observed values and true values are related by a generative model of the data.
This scheme is based on three assumptions: The brain minimizes a free energy of sensory inputs defined by a generative model.
This active inference scheme is based upon three assumptions: The brain minimises the free energy of sensory inputs defined by a generative model.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com