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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Vydáno v:BMC bioinformatics Ročník 13; číslo 1; s. 266
Hlavní autoři: Scheubert, Lena, Luštrek, Mitja, Schmidt, Rainer, Repsilber, Dirk, Fuellen, Georg
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 15.10.2012
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
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
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Shrnutí: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-13-266