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In order to discover time course change in gene expression patterns, a model-based method [ 30] was used for clustering the gene expression profiles.
17 22 While humans are very good at finding patterns, a model-driven approach provides a more precise and potentially more automatic framework for exploring missing data.
Ideally, the positive patterns of a model would cover exclusively positive observations, while its negative patterns would cover only negative observations.
More specifically, we show the existence of cell-sized Turing patterns in a model of glycolysis, using realistic parameter values and equal diffusion coefficients of ATP and ADP.
Here we analyze switching between patterns in a model network comprising relaxation oscillators interconnected by fast inhibitory synapses and electrical coupling.
One model proposed to explain this mosaic patterns is a model of repeated aggregation and subsequent duplication of genomic sequences.
Integrating such time dependent exposure patterns into a model of risk is a classical challenge frequently encountered by epidemiologists [ 1- 5].
Maize transcripts with expression patterns matching a model with a PCC value of 0.7 or greater were considered to have a circadian rhythm.
When inference regards a finite spatial pattern, a model-based framework is commonly used, even if the problem of model misspecification may occur.
The predicted emerging transient patterns offer a model-based analysis of phase-depended stimulation protocols.
Wu et al. [ 5] used a Markov chain model to account for the inherent dynamics of time-course gene expression patterns within a model-based clustering approach.
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