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The paper examines the uniqueness of seminal parametric design concepts, and their impact on models of parametric design thinking (PDT).
Ultimately, the ability to target particles to different parts of the cell nucleus may start to impact on models available for chromosome aberration formation and chromosomal organisation and mechanisms underlying genomic instability.
This has an impact on models for raising research funds.
Sequentially removing variables involved in multicollinearity had limited impact on models estimates for the main exposure.
Indeed, it may be possible to discount the influence of experimental artefacts by first exploring and understanding their impact on models.
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Assumptions about survivorship are often hidden; our results suggest that these modelling assumptions should be more explicitly stated given their impact on model dynamics and results.
We identified several key uncertain treatment-related parameters and assumptions which we varied systematically, first one at a time and then in combination, to assess their impact on model-projected survival (Table 1).
It was also found that they do not have a significant impact on model performance.
The selection of appropriate mathematical functions has high impact on model performance.
Influential data are those that have a disproportionate impact on model performance, parameters and/or predictions.
Hence, for each machine learning algorithm, the parameters with the greatest impact on model accuracy need to be identified.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com