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Table 1 Conditional entropies and their significances for segmentations on coding-noncoding data

From: Comparing segmentations by applying randomization techniques

P H (T|P) p p k H (P|T) p p k
L cd 0.219 0 0 0.284 0 0
E cd 0.286 0 0 0.143 0 0
L 2 0.473 0 0 0.353 0 0
L 1 0.566 0.0018 0.0005 0.364 0 0
L A 0.879 0.1642 0.4219 0.466 0 0
L C 1.212 1 0.9770 0.512 0 0.0001
L G 0.738 0.0522 0.0732 0.849 0.1195 0.0661
L T 0.455 0 0 0.289 0 0
  1. Conditional entropies and their significances for segmentations on coding-noncoding data. E cd : entropic segmentation with codon features. L f : least-squares segmentation with features f; f {cd, 2, 1, A, C, G, T} indicate the codon feature, frequencies of 2-letter words, 1-letter words, and frequency of A, C, G, or T, respectively, p : the fraction of segmentations from C N , k , MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaaruWrPXgBtfMBZbaceaGae83qam0aaSbaaSqaaGqaciab+5eaoHqaaiab9XcaSiab+TgaRjab+XcaSiabloriSbqabaaaaa@3698@ with a smaller value of the conditional entropy in 10,000 randomizations; p k : the fraction of segmentations from C N , k MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciaacaGaaeqabaqabeGadaaakeaaruWrPXgBtfMBZbaceaGae83qam0aaSbaaSqaaGqaciab+5eaoHqaaiab9XcaSiab+TgaRbqabaaaaa@348B@ with a smaller value of the conditional entropy in 10,000 randomizations.