Journal Article

DZNE-2020-03123

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets.

Scheubert, L. ; Luštrek, M. ; Schmidt, R. ; Repsilber, D. (Corresponding author) ; Fuellen, G. (Last author)DZNE*

2012
Springer Heidelberg

This record in other databases:    

Please use a persistent id in citations: doi:10.1186/1471-2105-13-266

Abstract: 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.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.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.

Keyword(s): Alzheimer Disease: genetics (MeSH) ; Artificial Intelligence: statistics & numerical data (MeSH) ; Gene Expression (MeSH) ; Gene Expression Profiling: statistics & numerical data (MeSH) ; Genetic Markers (MeSH) ; Humans (MeSH) ; Oligonucleotide Array Sequence Analysis: statistics & numerical data (MeSH) ; Support Vector Machine (MeSH) ; Tissue Array Analysis: statistics & numerical data (MeSH) ; Genetic Markers

---

Contributing Institute(s):

1. Rostock / Greifswald common (Rostock / Greifswald common)

Research Program(s):

1. 344 - Clinical and Health Care Research (POF3-344) (POF3-344)

Appears in the scientific report 2012

---

Database coverage:
; ; ; ; BIOSIS Previews ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Ebsco Academic Search ; IF < 5 ; JCR ; NCBI Molecular Biology Database ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

---