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Consequently the compared integral and differential inference models had the same overall number of degrees of freedom.
The χ2 criteria were calculated from Eq. (3) for the differential inference model and from Eq. (6) for the integral inference model.
To summarize the obtained results, we note that although the performance of the integral inference model differed depending on the system, it was always superior to the differential inference model.
We can conclude that the DBN performance is comparable to the performance of the differential inference model and both are outperformed by the integral inference model with either polynomial or exponential kernels.
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Another recently-published approach involves treating redundant probe sets as replicate measurements of the same gene in an attempt to improve statistical power in differential expression inference [23].
Based on differential network inference framework, the netSAM can be extended easily to a majority of currently known genetic diseases.
Because the differential expression inference is made at the gene level rather than at the probe level, our model is an extension of Kendziorski's work [ 46, 47].
To address the challenges concerning differential network inference using data-knowledge integrated approaches, we formulated the problem of learning the condition‐specific network structure and topological changes as a convex optimization problem.
It has been shown that biological averaging occurs for most of the transcripts and differential expression inferences are comparable for individuals and pools.
This also equalizes the degrees of freedom in the compared differential and integral inference models.
We reconstructed network structures from artificial and real experimental data using differential and integral inference models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com