Individualized markers optimize class prediction of microarray data.

ABSTRACT:

Background

Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker--selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample.

Results

Instead of using the same features for the classification of all samples, the pr

SUBMITTER: Pavlidis P 

PROVIDER: S-EPMC1569876 | biostudies-literature | 2006 Jul

REPOSITORIES: biostudies-literature