ライフサイエンスコーパス: extreme value

1 improved precision that tended to have less extreme values.

2 ariance, and their results are influenced by extreme values.

3 del parameters were adjusted to unreasonably extreme values.

4 nt and its enemies escalate to more and more extreme values.

5 b pairs when the parents also have similarly extreme values.

6 Here, using extreme value analysis, we find that the frequency of U.

7 ries of the polymorphism data, and loci with extreme values are considered to be likely targets of po

8 datasets are diverse but of modest size, and extreme values are often of interest.

9 trality tests and present formulas for these extreme values as a function of sample size and number o

10 luorescence resonance energy transfer) or an extreme value (as in cyclization), and in principle prov

11 Results Extreme values aside, results of histogram analysis of A

12 Compiled nonmonophyly rates are probably extreme values, because molecular analyses have focused

13 been justified by the statistical theory of extreme values, because the fitnesses conferred by benef

14 or binding-site matching by SOIPPA follow an extreme value distribution (EVD).

15 These estimates are derived using the extreme value distribution from the mean and variance of

16 An extreme value distribution model estimates the statistic

17 approximations for both the distribution and extreme value distribution of similarity scores.

18 alignments do not follow the classic Gumbel extreme value distribution, we propose a novel distribut

19 anscript isoforms to follow the same Weibull extreme value distribution.

20 that characterizes the binding signal as an extreme value distribution.

21 on new unbiased estimators for parameters of extreme value distribution.

22 e PDB and found that the TM-scores follow an extreme value distribution.

23 n of attraction of the so-called Gumbel-type extreme value distribution.

24 approximate the parameters of the underlying extreme value distribution.

25 using the local alignment version follow an extreme value distribution.

26 y our generalised scoring matrix followed an extreme value distribution; this yielded accurate estima

27 Furthermore, we show that by using the extreme-value distribution to characterize genomic regio

28 ity of essentiality for each gene, using the extreme-value distribution to characterize the statistic

29 is formula means it is unnecessary to fit an extreme-value distribution to simulations or to the resu

30 Structure comparison scores also follow an extreme-value distribution when the statistics are expre

31 These scores can be well described by an extreme-value distribution.

32 t the scores for sequence matching follow an extreme-value distribution.

33 We model this behavior by extreme value distributions with parameters that are lin

34 ity of occurrence, also in the context of ST extreme value distributions, and we conclude that rogue

35 We find that the similarity scores follow extreme value distributions.

36 We fit extreme-value distributions for fixed lengths of combine

37 ses, four model variables must be changed to extreme values for the cost-utility of annual screening

38 a problem, because the scores do not fit the extreme-value (Gumbel) distribution commonly used to est

39 with occult or obvious malignancy may be of extreme value in the detection and management of cancer

40 The influence of extreme values in the background data set can also be tu

41 quivalent and may be unavoidable at the most extreme values in this population.

42 Contributing factors that lead to extreme values include high geopolitical concentration o

43 ation (also called the 'Weibull' or 'Weibull extreme value' model) infers time to extinction from a t

44 When the extreme value of the density profile reaches rho = 0.5,

45 isplays significant regional variations with extreme values of 22% in the central gastrocnemius.

46 gy is to sequence only the subjects with the extreme values of a quantitative trait.

47 uate different approaches to describing more extreme values of body mass index (BMI)-for-age by using

48 nically characterized research subjects with extreme values of CSF Abeta levels.

49 We find that side-chain carboxyls with extreme values of koff or kon are involved in hydrogen b

50 Extreme values of Psi0 lead to asymmetric, bell-shaped e

51 didate genes or pathways in individuals with extreme values of quantitative phenotypes.

52 ective strategy is to sequence subjects with extreme values of quantitative traits or those with spec

53 e shifts in the probability distributions of extreme values of the Arctic and North Atlantic Oscillat

54 present a survey of methods for establishing extreme values of the group velocity, concentrating espe

55 Examination of the extreme values of these ratios indicates that probes wit

56 ntile for gestational age and postnatal day (extreme value) on at least 1 of the first 3 postnatal da

57 A second experiment in which one or other extreme-valued option was omitted from the learning sequ

58 Removing extreme values or adjustment for gender, cigarette smoki

59 , these data tables are often corrupted with extreme values (outliers), missing values, and non-norma

60 er potency that is technically similar to an extreme value statistic.

61 The extreme value statistics based method, while more genera

62 d an efficient algorithm for calculating the extreme value statistics for peptide identification appl

63 ial photosynthesis using currently-available extreme value statistics photon sources.

64 s that can be quantified in the framework of extreme value statistics.

65 In addition, the Gumbel extreme-value statistics are applied.

66 e discount rate are simultaneously varied to extreme values that bias the analysis against surgery.

67 The extreme value theory (EVT) has been widely used in study

68 This has been justified by using extreme value theory and, in particular, by assuming tha

69 vide some support for the use of Gumbel-type extreme value theory in studies of adaptation and point

70 w an elementary probabilistic model based on extreme value theory rationalizes the latter finding.

71 While the appeal to extreme value theory seems justified, the exponential di

72 However, extreme value theory shows that two other domains of att

73 Here we use extreme value theory to combine sea-level projections wi

74 By applying extreme value theory, Gillespie circumvented this issue

75 Using extreme value theory, I derive this distribution and sho

76 part because it can be justified in terms of extreme value theory, since beneficial mutations should

77 Yet in extreme value theory, there are three different limiting

78 In this study, we use extreme-value theory to explore the distribution of natu

79 This clears the way for asymptotic extreme-value theory to guarantee: (1) a non-increasing

80 We use analytic extreme value thresholds to identify a new class of indi

81 ugh we do not advocate hemodilution to these extreme values, we find that these data provide a physio

82 ghest incidence of co-occurrence and contain extreme values well above their local 95th percentile th

83 ucan levels above 6.7% and 10 below 3.6%.The extreme values were 1.8% and 7.5%.

84 erals, and statistically significant (P<.05) extreme values were reported for 14 of the 31 minerals t

85 d wide variability (range -0.04 to 1.0), and extreme values were seen in 34.5% of the group (<0.10 in

86 lds in the NHANES, which is a study in which extreme values were verified when recorded.

87 irst slowly increases and then points toward extreme values when the reproductive system tends toward

88 This principle suggests that extreme values will moderate the next time they are reco