With respect to the second challenge, a more accurate classifier will be achieved if the share of each kernel is not similar; and its weight is computed based on its efficiency in classification of samples.
Following the demand that the decision – making procedure should be rapid, simple and effective in classification of unknown samples, it is reasonable and justifiable to perform the second classification of the Vinča and Pločnik classes using the simplest classifier of the linear discriminatory function type.
In the NN group, CNFN and BUB3 were more frequently selected to define a split in the classification trees of the forest, whereas BTC, IL1F7 and TMEM99 (GI > 0.02) were important in classification of PN samples.
One of the challenges in classification of cancer tissue samples based on gene expression data is to establish an effective method that can select a parsimonious set of informative genes.
This level of accuracy in the classification of samples was achieved because of differences in the abundances of individual proteins between treatments.
The primary aim of this work is to show that the addition of the third dimension is useful, and brings extra information, helping in the classification of samples.
This is best exemplified by the modest agreement in the classification of samples (agreement of 64%, kappa score of 0.527, and 95% confidence interval of 0.456 to 0.597) when a cohort of 295 breast cancers was classified into the molecular subtypes by the authors of the original studies on the molecular classification using SSPs by Sorlie's [ 13, 30] and Perou's [ 26, 31] groups.
Our group [ 27] and others [ 28, 29] have demonstrated that subtle variations in data normalization and centering, as well as in the proportion of samples from each of the subtypes, may lead to changes in the classification of samples using SSPs.
Validation of this novel genomic marker to assist in classification of OC is required in a larger sample set.
This software tool allows the classification of samples in the high-dimensional parameter space by using support vector machine (SVM) learning.
