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


CONCLUSIONS
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.
Institute of Computer Science, University of Osnabr¨ uck, Albrechtstr. 28, 49076 Osnabrück, Germany.
Lena
L
Scheubert
Mitja
M
Luštrek
Rainer
R
Schmidt
Dirk
D
Repsilber
Georg
G
Fuellen
D000544:Alzheimer Disease / Q000235:genetics*; D001185:Artificial Intelligence / Q000706:statistics & numerical data*; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D005819:Genetic Markers; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D060388:Support Vector Machine; D046888:Tissue Array Analysis / Q000706:statistics & numerical data*
D005819:Genetic Markers