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...

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Veröffentlicht in:	BMC bioinformatics Jg. 13; H. 1; S. 266
Hauptverfasser:	Scheubert, Lena, Luštrek, Mitja, Schmidt, Rainer, Repsilber, Dirk, Fuellen, Georg
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 15.10.2012 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accuracy Algorithms Alzheimer Disease - genetics Alzheimer's disease Artificial Intelligence - statistics & numerical data Bioinformatics Biomarkers Biomedical and Life Sciences Brain Classification Comparative analysis Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data processing Gene Expression Gene Expression Profiling - statistics & numerical data Genes Genetic algorithms Genetic Markers Genetic research Genetic vectors Humans Investigations Life Sciences Machine learning Methods Microarrays Oligonucleotide Array Sequence Analysis - statistics & numerical data Research Article Sequence analysis (applications) Studies Support Vector Machine Tissue Array Analysis - statistics & numerical data Feature Selection Mutual Information Classification Accuracy Gene Pair Random Forest
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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