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We now discuss what may appear to be a special case of these multiple pathway models, but which are of sufficient flexibility to embrace most categories of multiple pathway models.
Such derivation by the traditional approach is extremely difficult and had not been attempted previously for multiple pathway models.
However, Tan and Tan [ 132] fitted very similar multiple pathway models to virtually the same SEER data and found stronger evidence for the involvement of genomic instability.
However, it is well documented that not all individuals with ADHD show impairment on all core tests of EF [ 12, 13], and multiple pathway models have been developed to link different aspects of EF to neurobiology [ 14, 15].
Most important is CytoSolve's core feature for integrating multiple pathway models, which can be distributed across multiple computing systems and solved in parallel, obviating the need to merge models into one system, running on a single computer.
As noted in the sub-section "Multiple pathway models incorporating genomic instability", there is little evidence to indicate that models with GI, let alone models that assume multiple types of GI, yield better fit than models that do not assume GI [ 6, 89] although conclusions at variance with this have been reached by other modelling groups [ 132].
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To demonstrate the advantages and disadvantages of this approach, we performed several simulation experiments using the spatially explicit, multiple pathway model a LANDscape Succession Model (LANDSUM) to quantify the range and variability in six class and landscape pattern metrics for four landscapes in the northwestern United States.
This shows that the multiple pathway model fits better than the single pathway 4-stage model as proposed by Luebeck and Moolgavkar [ 17].
Our results showed that the proposed multiple pathways model fitted better than the single pathway 4-stage model as proposed by Luebeck and Moolgavkar [ 17].
In this preliminary study, we have not yet compared the multiple pathways model with the single pathway model regarding prediction of future cancer cases and evaluation of treatment protocols for human colon cancer.
(The respective AIC and BIC for the multiple pathways model are 55.96 and 81.30 which are ten times smaller than those of the AIC (816.0667) and BIC (827.1513) respectively of the single pathway 4-stage model).
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multiple pathway components
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multiple pathway genes
multiple pathway aberrations
multiple business models
multiple regression models
multiple climate models
multiple pathway sensors
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