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We chose a 2 ms spacing between adjacent time points for the classifier.
Alterations in expression between adjacent time points across the entire time series were characterized using the trajectory clustering algorithm [14].
This approach generates a set of direction change labels between adjacent time points, namely increased, flat, or decreased expression, that can then be extended into a more informative matrix by including all time points.
Bold font indicates a minimum two-fold change between adjacent time points.
There was overlapping of spectra between adjacent time points as might be expected if a continuous phenotypic shift was occurring due to cell differentiation.
Another shortcoming is that the selected model occasionally abruptly changed between adjacent time points despite the phenotype following a smooth curve across time.
Similar(39)
Similarly, Kim [ 33] developed a difference-based clustering algorithm (DIB-C) in which the profile of short time-series data was discretized to symbolic patterns, but according to the differences between adjacent time-points.
Analysis of variance (ANOVA) for repeated measures was used to assess time differences in JOA and NDI scores across time points, while paired t test was used to compare score means between two adjacent time points.
An additional 12 genes were identified whose levels of expression changed at least two-fold between two adjacent time points and then changed in the opposite direction at least two-fold between the next adjacent time points.
And in all the comparisons between two adjacent time points in each passage, only day 1 and day 3 presented statistical difference (#p < 0.01).
Differences between two adjacent time points for the same genotype were determined using independent sample t-tests in IBM SPSS Statistics 19 (SPSS Inc., Chicago, IL, USA).
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