Sentence examples for models with sequence from inspiring English sources

218, 395 408) give important limits on the use of current models with sequence data for studying ancient aspects of evolution; but they go too far in suggesting that several fundamental aspects of evolutionary theory cannot be tested in a normal scientific manner.

After performing a sequence similarity search using FFAS [2] we identified two potential search models, with sequence identities of 19% (2GMY) and 24% (2O4D).

The statistical framework that we develop to combine mathematical models with sequencing data is fully general and can be used to analyze any RNA-seq data given an adequate continuous-state dynamic model.

The NB distribution is unarguably the most well suited distribution for read count data [ 24, 28] and, for this reason, our framework is inherently better suited for linking dynamic models with sequencing data than standard methods such as maximum likelihood fit via normal distribution.

A support vector machine (SVM) model with sequence and topological features was built to predict new interactions between HPV16 and human proteins.

Comparisons of sildenafil and N-desmethylsildenafil PK parameters with respect to placebo and sitaxentan treatments used an analysis of variance (anova) model with sequence, subject within sequence, treatment, and period as the classification variables.

The log-transformed values of UAER were analyzed by a PROC MIXED model with sequence, treatment, and period as fixed factors and subject (nested in sequence) as a random factor.

Comparisons of the PK of edoxaban administered with a potentially interacting drug versus edoxaban alone were performed using an analysis of variance (ANOVA) model with sequence, treatment, and period as fixed effects, and subject nested within sequence as a random effect.

For 24-h blood pressure data, daytime average, nighttime average, and 24-h average values for systolic blood pressure and diastolic blood pressure were analyzed using a PROC MIXED model with sequence, treatment, and period as fixed factors and subject (nested in sequence) as a random factor.

Treatment interaction by age and sex were analyzed, post hoc, among the ITT population using a linear mixed model with sequence, period, sex (or age), treatment, and treatment by sex (or age) defined as fixed effects and subject-within-sequence as the random effect.

These spread-versus-peaked tendencies of WAG and CAT probably have a direct influence on the way these two models deal with sequence saturation.