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The algorithm is able to capture the correlation between consecutive time slices in both score function and learning procedure, thus Friedman's score metrics gives a higher precision and recall than the naive REVEAL algorithm application in the absence or presence of preprocessed network generated by Zou&Conzen's algorithm.
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By introducing a stationary correlation between two consecutive time slices, Friedman's score function showed a higher precision and recall than the naive REVEAL algorithm.
We find the maximum score function and add a correlation between the factors in consecutive time slices or the same time slice if the relationship increases the score function.
The model is updated for consecutive time slices during the pass-by so that it is possible to study directional and time-dependent effects on the source characteristics.
Other approaches identified in the broader field of biomedical engineering deal with pairs of consecutive time-slices rather than representing the follow-up as a whole [ 14].
Moreover, we collect and analyze gene expression data from circulating blood cells throughout the follow-up period in consecutive time-slices, in order to model the temporal dimension of the disease.
It is determined by summing the mean value between consecutive time points multiplied by the period of time between consecutive time points and then dividing by the total time (see Figure 1).
with the period T frame between two consecutive time frames.
The interval "N" between two consecutive time steps can be calculated: N = T2-T1.
Maximal productivities represent the highest productivity values between two consecutive time points.
Using the stochastic model, we conclude that the uncertainty is highest between two consecutive time points.
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