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The literature discusses various reasons for this filtering failure.
For this filtering, the Kalman filter has been chosen, due to its ability to track a time-varying state.
We selected seven network centrality measures and two further similarity measures for this filtering step.
For this filtering step, all the degradome data sets were utilized at the same time to do a comprehensive screening.
The rationale for this filtering is that in each tissue some of the interacting TFs play general roles, since all tissues possess common mechanisms to control the fundamental biological processes.
As end-users of the tool, we could not precisely identify specific reasons for this filtering out and a detailed investigation of SSH algorithm implementation is out of scope of this study.
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The majority of applications take a blacklist approach – ie. check for that, check for this, filter, block, filter.. filter.
Another possible reason for this filter divergence could be the insufficient number of samples used.
Wiener weights are used to provide a minimum MSE estimate for this filter structure.
It is recommended to use a cut-off period of 10 20 sec for this filter to avoid antialiasing.
The simulation shows, close to, 100% dropping efficiency and suitable quality factor at 1644.7 nm wavelength achieved for this filter.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com