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Defining models of expression evolution is an important (and challenging) future goal.
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First, for misexpressed genes in hybrids the overwhelming preponderance of misexpressed genes follow a semi-dominant model of expression behavior because hybrid expression is intermediate and/or additive compared to expression differences between the two species (Table 2).
Using the expression levels, we build a statistical model of expression for each probe set, based on an assumed bimodal distribution, that accounts for the two states of an expressed gene: Up and Down.
Genes that are misexpressed in hybrids compared to each species follow a semi-dominant model of expression behavior because most genes have an intermediate level of expression in hybrids.
These results suggest a general pattern of intermediate expression in hybrids and are consistent with a semi-dominant model of expression difference even despite the strong asymmetrical pattern of misexpression in hybrids compared to the two parental species.
In short, stabilizing selection model of expression divergence is consistent with the nearly neutral model.
Arboretum does not provide, however, a model of expression evolution (only of co-expression evolution, by evolving module assignment).
This is the model of expression still found in Metazoa [ 96], S. pombe [ 19], and filamentous ascomycetes [ 92– 92].
Despite its relevance, however, statistical modeling of expression quantitative trait loci (eQTL) has not received the attention it deserves.
Our method uses a co-training algorithm to combine a model of expression similarity with a model of the text which accompanies the expression experiments.
This model of expression regulation can also be derived from a thermodynamic model of two transcription factors (a repressor and an activator) independently binding to a regulatory region.
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