Sentence examples for model with sequence from inspiring English sources

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.

Hatami et al., [5] developed this model with sequence-dependent setup time for first stage machines.

We extend the mathematical model proposed by Low and Yeh (2008) by changing it to a bi-objective model with sequence-dependent setup times.

It is also not too hard to show that if all typed versions of a generalized positive formula φ in the language of the intended model (with sequences s appearing as values of free variables replaced by their values at the appropriate types) are true, then the original formula φ is true in the intended model.

For the sake of comparability, we used the same evaluation scheme, applying leave-one-out (LOO) cross validation, in which we trained our model with sequences of eight actors and evaluated our proposed method with the sequences of the remaining one actor.

Following our analysis, we tested our assumption that the probe effects were additive by verifying the linearity and normality of our additive model with sequencing data.