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Here, we propose a new unsupervised and data-driven method, termed the eigenspace maximal information canonical correlation analysis (emiCCA), which is capable of automatically capturing the linear and/or nonlinear relationships between various data sets.
While a considerable quantity of data has been published on driving-point mechanical impedance, apparent mass and seat-to-head transmissibility magnitude and phase characteristics of seated subjects under vertical whole-body vibration, significant variation is known to exist between various data sets.
Differences between various data sets were tested for significance using Student's t-test and p-value of less than 0.05 were considered significant (* p < 0.05; ** p < 0.01; *** p < 0.001).
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Notably Kirby and Hasselbrink, pointed out this problem in their 2004 paper and proposed normalization of measured [zeta] potential by ionic strength of solutions which resulted in a better agreement between the various data sets.
Notwithstanding fast developments and hard work, challenging research opportunities are situated in (1) the creation and maintenance of the links between the various data sets and in (2) devising beneficial implementation approaches that rely on appropriate combinations of declarative and procedural programming techniques, semantic and legacy data formats, user input, and automated procedures.
However, our intention was to greatly minimize false positive and thus compare only those genes that were truly the same between the various data sets.
We found that ~11.5% (1750/15000) of human pseudogenes are transcribed (after correcting for pseudogene annotation overlaps within and between the various data sets) [see Additional file 1].
The range of the minimum and maximum proportion of patients reported in each category varied widely between the various data sets.
As a consequence, there is much commonality between the various data sets for individual cancers with minor differences reflecting differences in local practices or in interpretation.
This point is important, as there is relatively little difference between the models with respect to the mean values (whose experimental values are also well clustered between the various data sets).
The overlaps between these genes and various data sets were computed using the Molecular Signatures Database (MSigDB) [27].
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CEO of Professional Science Editing for Scientists @ prosciediting.com