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A diabody without C-terminal cysteine was generated to evaluate the effect of the additional cysteine.
A receiver operating characteristic (ROC) curve [58 60], that plots the true positive rate against the false positive rate, was generated to evaluate every model on both cross-validation and external validation test sets.
A Kaplan-Meier model was generated to evaluate survival.
A predictive nomogram was generated to evaluate the risk for overall survival (OS) of gastric cancer patients.
A linear regression of RT-PCR log fold change versus microarray log fold change was generated to evaluate the validity of the microarray data (Fig. 2B).
A logistic regression model was generated to evaluate the potential of serum cytokine profile in discriminating the CRC patients from the controls.
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Time activity curves were generated to evaluate the uptake and secretion pattern.
Two sets of benchmark instances were generated to evaluate the proposed algorithms.
For each figure, 250 random realization of different channel coefficients are generated to evaluate the average performance.
Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm.
Test cases with different job factors were generated to evaluate the algorithms and to demonstrate its strength.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com