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In our model, each gene constitutes a set of sextets and within each sextet, only the reference codon is allowed to evolve.
In Kauffman's [1] original NK model, each gene has exactly K inputs and in-degree distribution is therefore a Dirac delta function.
In this model, each gene is a nearly insignificant and faceless pawn, but in aggregate these gray pawns form an effective army.
In a Bayesian network model, each gene is considered as a random variable and the edges between a pair of genes represent the conditional dependencies entailed in the network structure.
It is important to highlight that, in this multivariate model, each gene may depend not only on its own past values, but, also, on the past values of the other genes.
Using FDR corrected p-values (FDR ≤ 0.15) from this model, each gene from the AD1 and F1 samples was categorized as 'A-biased' (log ratio ((ln(Aprobe) – ln(Dprobe)) statistically greater than 1 1 mix), 'D-biased' (log ratio statistically less than 1 1 mix) or 'Equivalent' (log ratio not statistically different from 1 1 mix); see Figure 2A.
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In the proposed models, each gene represents a channel state and the channels of a node form a chromosome.
However, by modeling each gene individually, we have shown that TF combinations detected in one cell type could in some cases predict gene expression in other cell types.
When the F/RPKM values are log transformed, the magnitude differences will be decreased and the importance of the preparation factor for modeling each gene expression estimate will appear smaller relative to other factors.
The contigs generated in Trinity are by default clustered in "trinity components" or gene models, where each gene model represents a putative gene.
We conducted an LRT between the null model and the alternative model for each gene.
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system each gene
example each gene
pattern each gene
sample each gene
model each genome
model each sequence
models each gene
model each step
model each node
model each coupling
model each time
model each stair
model each material
model each test
model each object
model each client
model each state
model each parameter
model each layer
model each atom
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