Sentence examples for microarray classification shows from inspiring English sources

A comparative study of several different error estimation techniques on microarray classification shows that 0.632 + bootstrap can be more appropriate than other estimators including resubstitution estimator, cross-validation, and leave-one-out estimation [ 39].

Similarly, a real-time Q-RT-PCR assay has been recently shown to recapitulate the microarray classification of breast cancers [ 19].

These methods are common used in microarray classification problems [ 49- 51].

Usually, microarray classification process comprised of two successive steps, that is, feature selection and classification.

In this work, we addressed RotBoost ensemble classification method to cope with gene microarray classification problems.

In the present work, we proposed a novel ANMM4CBR method for microarray classification.

Experimental results on microarray data sets for tumor classification showed that genetic algorithm based multi-task learning performed better than H-MTL, a heuristic multi-task learning method, and GA-FS, a naive feature selection method based on genetic algorithms.

This classification showed 89.4% correct classification of species.

A recent comparison of feature selection methods for multiclass microarray data classification (Chai and Domeniconi, 2004) shows that wrapper methods such as SVM-RFE lead to better classification accuracy for large number of features, but often gives lower accuracy than filter methods when the number of selected features is very small.

Canedo et al. [5] propose an ensemble of filters and classifiers for microarray data classification.

IN microarray experiments, classification of data is a crucial step for the prediction of phenotype of cells.