Sentence examples for microarray classification problems from inspiring English sources

The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains.

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

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

Using a continuous-value version of the UMDA procedure, EDAs have been used as a new way of regularizing the logistic regression model for microarray classification problems [ 46].

For this reason, it was postulated that PLS might perform better than PC in microarray classification problem and indeed, successful results of microarray gene component classification with PLS had been reported for several human cancers in past few years [ 25, 26].

On the other hand, Nguyen and Rocke performed binary and polychotomous LR and linear/quadratic discriminative analysis from PLS scores for two-class and multi-class microarray tumor classification problems [ 5, 6].

Independent component analysis (ICA) has been widely used to tackle the microarray dataset classification problem, but there still exists an unsolved problem that the independent component (IC) sets may not be reproducible after different ICA transformations.

In a microarray data classification problem, we are given N training samples, where x i is an M-dimensional vector in the feature space and y i ∈ {0, ⋯ K - 1} is the class label.

A well known problem in applying microarrays in classification problem is dimension of obtained datasets.

MDR may be applicable to many other classification problems for microarray data.

In the problem of microarray classification, however, except the simplest form NN, CBR classifiers were considered in only a few literatures [ 11, 12] and was only tested on some simple data sets.