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12 These findings have been contested because relevant but negative data were ignored.
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Machine learning is important for pre-miRNA detection, but negative data is of an unknown quality [5], which highlights the need for models that do not depend on negative data.
Here, only negative data were analyzed using similar optimized parameters, for reasons of better predictability without need for use of authentic standards [14, 30].
The homologous negative data were also reduced by using the same approach.
For the PSSM algorithm [41], the probabilities of the twenty amino acids in terms of positive data and negative data were calculated as P+ and P−.
Nonredundant negative data were generated using the same approach.
The nonhomologous negative data were generated using the same approach as the positive one.
The suffix ' ' indicates that only positive data were used and '±' indicates that positive and negative data were used.
Ninety-three (42%) tumors were positive, 51 (23%) displayed moderate staining and 58 (26%) were negative (data were missing for 21 (9%) tumors).
A total of 113 (51%) tumors were NAT1 positive and 91 (41%) tumors were NAT1 negative (data were missing for 19 (8%) tumors).
Transmission of Orientalis but not Antiqua or Medievalis organisms did not result merely from experimental bias because negative controls remained negative, data were duplicated, rabbits exhibited equivalent bacteremia, and lice took equivalent blood meals regardless of biotype.
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