To further extend and complement our analysis we investigate the dynamics (i.e., stability and changes) of the recipe and ingredient frequency vectors between consecutive months using the rank biased overlap (RBO) metric.
To go beyond the exploration of the popularity of selected ingredients, we next explore the dynamics (i.e., stability and changes) of the recipe and ingredient frequency vectors between consecutive weekdays and months using the rank biased overlap (RBO) metric [23].
However, estimates of N b obtained by the LD method may be biased by overlapping generations and fluctuating population size (Waples et al. 2014).
The central outcome of our study points to a splicing dichotomy between human alternative 5'ss and 3'ss exons in that they were markedly biased toward overlapping splice sites, with A5Es biased for E = 4 nucleotides (tandem donors, A5EΔ4), in contrast to A3Es biased for E = 3 nucleotides (tandem acceptors, A3EΔ3).
In fields that use statistical inference to test the experimental hypothesis (which, as discussed above, tend to be the "softer" ones), the positive-outcome bias overlaps with a more generic bias against statistically non-significant results (i.e. results that fail to reject the null hypothesis), which is well documented in many disciplines [43].
However, when it comes to the structural determination of the genome and proteins within the capsid, the orientation determination of the non-symmetric genome information will be biased by the overlapping high-symmetry capsid information in the virus particle images.
This is where study publication bias and outcome reporting bias overlap.
As an alternative approach to exclude potential bias, overlap between the NSCLC and MCF-7 data sets was examined using probes randomly selected from the NSCLC expression arrays, which did not qualitatively affect the results reported.
As I was the only researcher involved in data collection and analysis, coding and recoding of the transcripts were performed to ensure data reliability, and that the categories and themes were free of ambiguity, bias, overlap and lack of clarity.
For these reasons, we prefer to choose MaxEnt modeling as our machine learning algorithm for sentiment detection over other models, which are biased by having overlapping features (e.g., Naive Bayes models) or are used as uninterpretable black boxes (e.g., SVM or RNN).
