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Numerical results show that this approach can generally predict the behavior of interlaminar stresses.
Similar results were obtained upon expression of a recombinant fragment of the ErbB2/HER2 receptor protein or GST as MBP fusions, indicating that this approach can generally be applied to enhance secretion and yields of functionally active recombinant proteins.
In the next section, we first show that the approach can generally be extended to sets of PFMs.
Analysis of limited gene panels (regardless of the sequencing approach) can generally be done under existing consent procedures and is essentially not different from evaluation of genes using traditional Sanger sequencing.
Regarding to the classification performance, the gene clusters produced by our approach can generally achieve good cross validation performance compared to the existing methods for most of datasets we tested.
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The above results suggest that the use of LLL approaches can generally lead to an improvement in LD50 prediction accuracy.
Although model-driven and data-driven approaches can generally complement each other (e.g., [36]), only one paradigm is used at a time [16, 17, 30, 34].
For graph classification, brain network analysis approaches can generally be put into three groups: (1) extracting some local measures (e.g., clustering coefficient) to train a standard vector-based classifier; (2) directly adopting graph kernels for classification; and (3) finding discriminative subgraph patterns.
These approaches can generally be grouped into two larger categories: logical and continuous models.
Their approaches can generally be classified into two categories.
The approach can be generally used for different kinds of coated fabric.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com