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The paper is organized as follows: First, we describe the framework used for model selection and identification, based on the nested models paradigm.
A breakthrough work, Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM), 60 integrates copy number, mRNA expression, and pathway interaction data to a personalized pathway-by-sample matrix that clusters patients into distinct prognostic subgroups.
Kristensen et al. [ 4] used an integrated approach termed 'Pathway Recognition Algorithm using Data Integration on Genomic Models' (PARADIGM), integrating DNA methylation and microRNA profiling with mRNA expression and DNA copy number.
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A new modeling paradigm – multiscale modeling – has appeared that may alleviate these problems.
A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
Moreover, the novel model paradigm established here is well-suited to further mechanistic and drug screening research applications.
The tool relies on fault propagation models and an object-oriented modelling paradigm.
The paper introduces the new modeling paradigm and briefly demonstrates some of its principles and applications.
These techniques provide an accurate and efficient modelling paradigm for machining processes.
Within the dataflow modeling paradigm, the CONVERGENCE Simulation Architecture uniquely supports a combination of synchronous dataflow (SDF) models and dynamic dataflow (DDF) models.
This paper argues that this problem can be solved by making a commitment to deterministic models that embrace temporal properties as an integral part of the modeling paradigm.
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