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Open Access Highly Accessed Research article

Tissue-based Alzheimer gene expression markers–comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets

Lena Scheubert13, Mitja Luštrek2, Rainer Schmidt3, Dirk Repsilber4* and Georg Fuellen35*

Author Affiliations

1 Institute of Computer Science, University of Osnabrück, Albrechtstr. 28, 49076 Osnabrück, Germany

2 Department of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia

3 Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Ernst-Heydemann-Str. 8, 18057 Rostock, Germany

4 Leibniz Institute for Farm Animal Biology (FBN Dummerstorf), , Wilhelm-Stahl Allee 2, 18196 Dummerstorf, Germany

5 DZNE, German Center for Neurodegenerative Disorders, Gehlsheimer Strasse 20, 18147 Rostock, Germany

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BMC Bioinformatics 2012, 13:266  doi:10.1186/1471-2105-13-266

Published: 15 October 2012

Abstract

Background

Alzheimer’s disease has been known for more than 100 years and the underlying molecular mechanisms are not yet completely understood. The identification of genes involved in the processes in Alzheimer affected brain is an important step towards such an understanding. Genes differentially expressed in diseased and healthy brains are promising candidates.

Results

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.

Conclusion

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. The biological significance of the genes and gene pairs is discussed.