Sentence examples for in combination with classification from inspiring English sources

GA procedures in combination with classification methods have been successfully used in the analysis of microarray data [ 26, 27].

Edge or region based approaches [ 12– 14] either alone or in combination with classification approaches [ 15– 18], which are based only T2W or T1W images, are more suitable for segmentation of pre-operative LGGs.

The analysis of wine blends obtained from binary mixtures of a wines using NMR spectroscopy in combination with linear classification models (LDA) and neural networks has also been reported [59].

To develop such signatures we have initially tested a univariate variable selection strategy based on an F test in combination with several classification methods (SVM, DLDA, PAMR, KNN, SOM) as implemented in the software application Prophet available in the Web based microarray analysis suite GEPAS [53].

All feature-selection methods performed similarly in combination with various classification algorithms for a given endpoint.

These findings suggest that our HMMs are useful, in combination with our classification models, for determining the origin of bHLH domains in complex samples.

To achieve a multi-level categorization of gene products, we utilized Gene Ontology terms [ 36] in combination with the classification system of FunCat [ 37].

By contrast, using negative stain EM in combination with image classification techniques it was straightforward to separate images of the two different molecules.

Our findings point to the fact that YB-1 expression in combination with histological classification in a double stratification strategy is superior to classical grading in the prediction of tumour progression in HNSCC.

Parametric per-pixel supervised (maximum likelihood) classification methods are used in combination with object-based classification methods to map urban features over New York City.

Recall that we consider three different feature selection techniques, the Lasso, SNSS, and RDA, in combination with three different classification approaches: (1) using the same model for classification as for feature selection, (2) using the selected features in a KNN classifier, or (3) using the selected features in a Random Forests classifier.