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Results: In this article, we consider the problem of learning a multitask regression model while taking advantage of the prior information on structures on both the inputs (genetic variations) and outputs (expression levels).
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The resultant approximate output expression was used to form an approximate log-likelihood function.
Using this expression, together with our input-output expression (52), allows us toobtain an expression for the output fluctuation of the microwave cavity in terms ofthe input, [ a ˘ ˆ o a ˘ ˆ o † ] = G [ a ˜ ˆ i a ˜ ˆ i † ], (57).
In the process of transcriptional regulation, the (output) expression level of regulated genes acts as a sensor for the (input) concentration of transcription factors.
From the output, expression differences between the samples being compared were requested at 90%, 95%, or 99% confidence levels.
The correction for saturation and probe-specific non-specific background assures linearity between the input (transcript concentration) and output (expression degree) measures.
We also provide a BLAST and batch searching facilities to output expression values for larger lists of genes that may then be used for further down-stream analysis.
This model describes how the upstream regulatory genes control their target genes to produce the output expression of mRNA through transcriptional regulatory network.
These models are specific to individual enhancers: they capture how genomic loci interpret TF concentrations to control the output expression level of their target genes.
The update function ϕ i constitutes the gene's signal-integration logic and can be represented as a look-up table that maps all of the 2 N possible combinations of input expression states to an output expression state.
As depicted in Figure 1B, a resource is a bit string, and as an abstraction of metabolic activity an individual has to reproduce the bit string as a temporal output (expression) pattern of its gene regulatory network.
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