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In this work, aspects of PK, physiologically based modeling, tumor physiology, and cell-level mechanistic modeling were combined to construct a model for liposomal drug delivery.
Specifically, d e novo and homology-based modeling were combined in an iterative manner with one-dimensional H nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopy, mutagenesis, biochemical measurements, and FRET microscopy.
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For example, practically all parts of the ROC curve become equally important when our sequence-based probabilistic modeling is combined with gene expression data and other information sources.
In such semiphysiological PK modeling approaches, the ease of analyzing outcome measures with population PK modeling is combined with the mechanistic insight of PBPK modeling.
The resulting boosted phoneme models were combined into a single speech recognition model using multistream techniques.
The resulting models were combined in model ensembles using two techniques, namely: greedy optimization and model stacking.
The probability values of the models were combined using four different consensus methods.
Stratified sampling and species abundance models were combined in this study.
In order to predict the concentrations of nutrients, DO, iron, and plankton, three models were combined.
Finally, the two models were combined to attain simulations that compared well with real measurements.
Kinetic and radiation models were combined with the corresponding mass balances to describe the experimental results.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com