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Using the cost model requires estimates of data sizes and cpu costs.
Section four studies the impact of data sizes and observational noise.
In this study, using S. cerevisiae as a model system, NCA was successfully applied to infer similar regulatory actions of transcription factor activities from two various microarray databases and several partial transcription factor-gene connectivity datasets for selected cytokinesis-related genes independent of data sizes.
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Second, the data are updated with a dramatic speed, in terms of data size and data distribution.
They also discuss the impact of data size on training and classification steps.
Furthermore, these approaches are often computationally expensive with the increase of data size.
Effect of data size is investigated to quantify the error due to the limited measured data.
This often leads us to a negative spiral of data size and understanding of the target phenomena.
Uncertainty prevails in the collected data set which warrants expansion of data size (population) by stochastic simulation process.
The transfer learning solutions surveyed are independent of data size and can be applied to big data environments.
Effect of data size and various k values In Fig. 7, we study the effect of data size and various k values for RkNN. Figure 7a, b show average total time and I/O cost for both filtering and verification phases.
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