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Strict approaches for data validation and allele calling to distinguish true alleles from artefacts are required.
Therefore, a rigorous 454 quality control is essential and strict approaches for data validation and allele calling are required to distinguish true alleles from artefacts [ 20, 21].
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The paper demonstrates how this approach may be used for data validation and integration, and in applications of "reverse modelling", allowing estimation of pore structure parameters, porosity and permeability, for a particular geological situation.
There are various approaches to data validation and these are highlighted in the medical literature [ 4- 13].
Though a team does check new data for formatting and new organisations, data validation is currently absent.
The level of data validation is minimal and sufficient for testing; a comprehensive data validation approach has been outlined for implementation prior to production release.
A large data set collected from about 200 IGS stations over one month in 2010 is processed with the new approach and several commonly adopted approaches for validation.
The improved model version had a better prediction of phenological (BBCH) stages compared to the original CW approach for an independent validation data set.
The transcriptomic data provide an independent approach for both validation and correction of the gene models.
Royston et al.[ 17] originally proposed the internal-external validation approach for survival data, within a framework to construct and validate a prognostic survival model from an IPD meta-analysis [ 12].
Currently, besides assessing its size and connectivity, the standard approach for GEM validation is to compare model predictions with experimental data under different conditions.
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