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We present a novel normalization method suited to equidistantly or un-equidistantly distributed probes on tiling arrays.
These authors proposed a novel normalization method based on the selection of invariant microRNAs that were subsequently used to normalize the arrays.
We propose cnvOffseq, a novel normalization framework for off-target read depth that is based on local adaptive singular value decomposition (SVD).
We explore the effect of varying read depth on transcript detection and quantitation, and offer a novel normalization method that robustly identifies the subset of active genes observed in an RNA-seq experiment and provides guidance regarding efficient experimental design.
Further, we investigated other novel normalization approaches which, to our knowledge, have not been applied to imaging data, such as median normalization or TIC normalization with manual exclusion of mass ranges.
Instead, we developed and applied a novel normalization method using the male pool as a reference, and with this normalization were able to derive the copy number, upper and lower bounds, and the significance of any deviation (Methods).
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Several advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions.
Several notable advantages of QChIPat include: 1) this software is able to make use of the information in control experiment; 2) this software incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; and 3) this software provides the binding pattern information among different enriched regions.
The new software incorporates a semi-automated dose calibration system, and a number of novel dose normalization methods.
It shows that by using the novel fluorescence normalization method and reliance on the more selected data points fluorescence ratio analysis, significant improvement in precision and repeatability was demonstrated in the novel analysis method compared to conventional method although the proposed strategy does not claim to completely replace the conventional method.
Our repeated observation of such examples has motivated our development of the novel weighted quantile normalization method (see the " Weighted quantile normalization" section) that properly removes any technical variations, while preserving important biological information with regard to expression differences, and further allowing us to account for additional covariates.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com