approach
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@MISC{Expression_approach,
author = {Gene Expression and Irit Fishel and Alon Kaufman},
title = {approach},
year = {}
}
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Abstract
Motivation: With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately. Results: We propose a new meta-analysis technique that aims at finding a set of classifying genes, whose expression level may be used to answering the classification question in hand. Specifically, we apply our method to two independent lung cancer microarray data sets and identify a joint core subset of genes which putatively play an important role in tumor genesis of the lung. The robustness of the identified joint core set is demonstrated on a third unseen lung cancer data set, where it leads to successful classification using very few top-ranked genes. Identifying such a set of genes is of significant importance when searching for biologically meaningful biomarkers. Contact:
Keyphrases
important role top-ranked gene common theme successful classification informative result new meta-analysis technique tumor genesis cancer microarray data set low sample size expression level integrative computational method identified joint core set multiple independent microarray data set joint core subset meta-analysis technique meaningful biomarkers classification question significant importance
