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Based on the unscented Kalman filter, Malcolm et al. presented a filtering strategy that treats model estimation and tractography as a causal process [43, 44].
In order to convert an interlaced image into a display format image, a filtering strategy, which resizes images with arbitrary ratios and reduces the overall computational load, is performed region adaptively using local characteristics such as motion or motionless regions.
In order to define a method to recursively estimate x ̃ t from the sampling set ( y t i, ω t i ) i = 1 N s, a filtering strategy has to be set.
Such a filtering strategy places the potential somatic mutation(s) in the contaminated cell into the inferred sequence of the contaminant and cannot distinguish the shared heteroplasmic mutations between the contaminant and the cell.
In these cases, a filtering strategy would not be warranted.
It is not necessary to devise a filtering strategy for all RNA-Seq experiments.
We proposed a filtering strategy based on the results of TransCount.
In summary, MetaCluster 5.0 uses a filtering strategy to remove noise from the extremely low-abundance species.
Therefore, we integrated a filtering strategy that can be used to remove probable recurrent or correlated artifacts based on strand bias.
For approaches which due not account for bias in the statistical model, a filtering strategy based on both ambiguity and allele assignment error has a small effect on the resulting percentage of false positives.
In addition, all methods tend to generate some false-positive splits when there are some random mutations or sequence errors, and thus a filtering strategy should be applied to remove the false positives while keeping the final results interpretable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com