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Different from the mathematical interpolation function based human body synthesis methods, the models generated in our method have the approximation errors minimized.

However, as with all machine learning methods, the models are likely to be most relevant within their domain of applicability and are likely to perform less well against novel molecules that are very different from those in the training set.

While that type of informatic simulation of metagenomic reads is a useful approach for benchmarking different binning methods, the models used for such simulations simply can not capture all factors affecting read sampling from a real metagenome sequencing experiment.

These monogenic variants are much easier to map than congenic genes, and because the mutations concerned are not as severe as knock-out or knock-in methods, the models are more physiological.

The structural models of skeletal muscle thin filament are well studied by 3D-EM and FRET methods, the models are constructed for blocked and closed states or states with and without calcium.

In addition to these built-in methods, the models are also subjected to processing by MolProbity (31) and an in-house secondary structure agreement (SSAG) assessment based on the work by Eramian et al.

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Methods: the model was developed using the WinBUGS software.

The letter is organized as follows: in section "Model data and methods" the model, simulations, and analysis methods are described.

For non-recursive methods, the model trained in DB can be directly applied to the next time domain.

Methods: The model consisted of a maternal and a child lipid compartment connected through placental diffusion and breastfeeding.

Second, contrary to conventional methods, the model enables us to represent and understand implicit temporal information (P2).

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