Sentence examples for multiple independent models from inspiring English sources

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The use of multiple independent models is also fundamental to other ensemble predictors, such as Random Forest, and to consensus methods for rescoring docked protein ligand complexes [23].

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However, both methods will work for multiple independent variable models where the independent variables can be either categorical or quantitative.

Another study, using multiple independent mathematical models in four settings-South Africa, Zambia, India, and Vietnam- concluded that early initiation of ART (at CD4 levels ≤500 cells/μL) is more cost-effective than late treatment [ 9].

To avoid possible bias associated with any single set of training positives we chose to train multiple independent scoring models, each based on a different set of training positives and corresponding negatives.

The improved prediction abilities of EJF over JF indicated that the genetic architecture can be better depicted by averaging multiple independent QTL models developed from disjoint subset maps as achieved by TAGGING.

We anticipate that, applied to (potentially multiple) independent QTL, Diploffect models could provide more robust out-of-sample predictions of the phenotype value in, e.g., proposed crosses of multiparental recombinant inbred lines than would be possible using ROP-based models.

Multiple calculations of independent models with the ab initio shape determination programs, DAMMIN and GASBOR, yielded consistent SAXS envelopes with only small variations between runs (Svergun, 1999; Svergun et al, 2001) (Supplementary Figure S10A B).

The multiple independent parallel reaction model was applied to predict the volatiles production.

γ-Secretase-dependent phenotypes of specific PSEN mutations that have been investigated in multiple independent studies or model systems are summarized in Table 3. PSEN mutations were chosen based on their investigation in multiple independent studies and model systems.

Xu [9] proposed a fixed model and a random model for multiple independent families based on the weighted least square method of linear model.

Pseudo-marginal methods instead integrate across the missing data using a Monte Carlo estimate for the likelihood, generated from multiple independent simulations from the model.

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