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

BMC Bioinformatics

Research Article

Mitja Luštrek¹  Rainer Schmidt²  Georg Fuellen³  Lena Scheubert⁴  Dirk Repsilber⁵ 

[1] Department of Intelligent Systems, Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia;Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Ernst-Heydemann-Str. 8, 18057, Rostock, Germany;Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Ernst-Heydemann-Str. 8, 18057, Rostock, Germany;DZNE, German Center for Neurodegenerative Disorders, Gehlsheimer Strasse 20, 18147, Rostock, Germany;Institute of Computer Science, University of Osnabrück, Albrechtstr. 28, 49076, Osnabrück, Germany;Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Ernst-Heydemann-Str. 8, 18057, Rostock, Germany;Leibniz Institute for Farm Animal Biology (FBN Dummerstorf), Wilhelm-Stahl Allee 2, 18196, Dummerstorf, Germany;

关键词: Feature Selection;    Classification Accuracy;    Mutual Information;    Random Forest;    Gene Pair;   

DOI  :  10.1186/1471-2105-13-266

 received in 2012-04-05, accepted in 2012-09-12,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundAlzheimer’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.ResultsBased 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.ConclusionCompared 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.

【 授权许可】

Unknown   
© Scheubert et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.