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A linear QSAR model was developed using Multiple Linear Regression technique, while Genetic Algorithm was adopted for selecting the most appropriate descriptors.
As it was mentioned previously, when a physical chemistry expert develops a QSAR/QSPR predictive model, the choice of the most appropriate descriptors for this model constitutes the first complex challenge.
A well-established feature selection method, the recursive feature elimination (RFE) method, was used to select the most appropriate descriptors for classification from a large pool of descriptors, and two evaluation methods, 5-fold cross-validation and independent evaluation, were used to assess the performances of these developed models.
For each cough they selected patient gender, the most appropriate descriptors and a diagnosis.
The aim of this study, therefore, was to determine the most appropriate descriptors for the reporting of an ICC, by surveying researchers working in the field of cluster trials.
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Local feature detector detects points or regions covariant with a class of transformations, and local feature description selects the most appropriate descriptor to characterize the points or regions.
Thus the most appropriate biological descriptors to diagnose the effects associated with the organic enrichment were the multispecies abundance data as well as the primary biological variables species richness and abundance.
Further research into the descriptors most appropriate to the reporting of the related k-statistic would be beneficial.
We also question which techniques - kinematics, kinetics or muscular activity – and descriptors are most appropriate for describing intrinsic movement variability and for integration into design tools.
Among the impressive amount of available computational drug discovery technologies, quantitative structure activity relationship approaches that rely on computational quantum chemistry descriptors are the most appropriate to model adaptive drugs.
In the following, we focus our attention on studying the most appropriate features to propose a robust fused descriptor representing driver head pose.
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most appropriate indicators
most appropriate indexes
most common descriptors
most diverse descriptors
most quality descriptors
most feasible descriptors
most basic descriptors
most influential descriptors
most appropriate models
most important descriptors
most effective descriptors
most detailed descriptors
most appropriate moments
most appropriate condiments
most representative descriptors
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CEO of Professional Science Editing for Scientists @ prosciediting.com