oru.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets
Institute of Computer Science, University of Osnabrück, Osnabrück, Germany; Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany.
Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany.
Leibniz Institute for Farm Animal Biology (FBN Dummersdorf), Dummerstorf, Germany.ORCID iD: 0000-0002-7173-5579
Show others and affiliations
2012 (English)In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 13, no 1, article id 266Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
London, UK: BioMed Central, 2012. Vol. 13, no 1, article id 266
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:oru:diva-40614DOI: 10.1186/1471-2105-13-266ISI: 000315045800001PubMedID: 23066814Scopus ID: 2-s2.0-84867384165OAI: oai:DiVA.org:oru-40614DiVA, id: diva2:777922
Available from: 2015-01-09 Created: 2015-01-09 Last updated: 2018-05-14Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMedScopus

Authority records BETA

Repsilber, Dirk

Search in DiVA

By author/editor
Repsilber, Dirk
In the same journal
BMC Bioinformatics
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 49 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf