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These two insights are founded in the same observation.
MDL combines these two insights by viewing learning as data compression: it tells us that, for a given set of hypotheses (mathcal H ) and data set (mathcal D ), we should try to find the hypothesis or combination of hypotheses in (mathcal H ) that compresses (mathcal D ) most [15, 28, 41].
Based on these two insights we hypothesized that to combine a rather strict network metric (here: dense connectivity) with a more relaxed gene expression metric (our definition of co-expression is little restrictive) may result in an excellent, while at the same time computationally manageable definition of a functional module.
These two insights provide the basis for a new methodology to estimate a human elimination half-life, t1/2elim, that is representative of the general adult population under background exposure conditions.
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The combination of these three insights would become Todd's future, and what he would eventually consider to be the blessings that came out of his cancer.
These three insights are partially verified by relevant research literature [ 7, 8, 36].
These data provide two insights: Firstly, reduced whole blood aggregation in septic shock was independent of molecular evidence of reduced actin-myosin contraction (no change in myosin LC20 phosphorylation), instead suggesting impairment of other elements of platelet function such as adhesion or granule secretion.
From these results, we obtain two insights.
The proof relies on two insights.
We explore two insights.
We can further obtain another two insights.
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