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In order for change to be detected in a statistically robust manner, the data analysis methods need careful consideration.
This will introduce the problem of HNPCC detection; MSI cases detected with previous methods need careful and laborious evaluation to correctly assess the possible risk of hereditary cancer.
Note, however, that such methods need careful handling from a statistical point of view (e.g. Altman and Royston 2000; Broadhurst and Kell 2006; Hastie et al. 2001; Ioannidis 2005, 2008).
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This method needs careful analysis by agencies legal affairs personnel, as there can be other liabilities incurred.
Moreover, while these latter segmentation methods often need careful (sometimes even manual) close-enough initialization to ensure the method convergence to a proper solution, the proposed approach can start with random initial values.
Instead, results from the two methods need more careful investigation.
Thus, at least until assembly and annotation methods become error-free, the results of high-throughput data collection and analyses often need careful investigation and/or experimental validation.
Such studies need careful design.
Projects also need careful planning.
These figures need careful interpretation.
The figures need careful parsing.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com