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Genes differentially expressed in diseased and healthy brains are promising candidates.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Based on microarray data we identify potential biomarkers as well as biomarker combinations using three feature selection methods: information gain, mean decrease accuracy of random forest and a wrapper of genetic algorithm and support vector machine (GA\/SVM). Information gain and random forest are two commonly used methods. We compare their output to the results obtained from GA\/SVM. GA\/SVM is rarely used for the analysis of microarray data, but it is able to identify genes capable of classifying tissues into different classes at least as well as the two reference methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Compared to the other methods, GA\/SVM has the advantage of finding small, less redundant sets of genes that, in combination, show superior classification characteristics. 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