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The authors also introduce a multiple reference scoring scheme by creating a frequency-weighted fingerprint from many reference structures.
With respect to the scoring, we wanted to incorporate the lessons learned from studies such as [14], e.g. usage of multiple reference scoring coupled with frequency based weighting of the interactions.
Besides, the good performance of the PADIF approaches demonstrates that (1) it is legitimate and useful to exploit the per atom score contributions of GOLD scoring functions for building interaction fingerprints and (2) that the employed multiple reference scoring combined with frequency based weighting seems to be a robust and promising way for ranking poses.
For PADIF, a multiple reference scoring was employed (reference complexes in Additional file 1: S3).
Such discrepancies will be difficult to predict prospectively and advocate the usage of a multiple reference scoring like in the PADIF approach.
The PADIF approach utilises the protein per atom score contributions of the GOLD scoring functions, enables a multiple reference scoring with weighting and showed superior performance.
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Multiple reference gene normalization was chosen.
This highlights the benefits of having multiple reference strains.
Ideally, normalisation should be performed against validated multiple reference genes.
When studies used multiple reference tests, we used intraoperative findings as the reference standard.
We also allowed correspondences between multiple predicted transcripts and multiple reference transcripts.
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