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1000 Titel
  • Tissue-based Alzheimer gene expression markers–comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets
1000 Autor/in
  1. Scheubert, Lena |
  2. Luštrek, Mitja |
  3. Schmidt, Rainer |
  4. Repsilber, Dirk |
  5. Fuellen, Georg |
1000 Erscheinungsjahr 2012
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2012-10-15
1000 Erschienen in
1000 Quellenangabe
  • 13: 266
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2016
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/1471-2105-13-266 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3574043/ |
1000 Ergänzendes Material
  • https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-13-266#Declarations |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • 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.
1000 Fachgruppe
  1. Medizin |
  2. Biologie |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/U2NoZXViZXJ0LCBMZW5h|https://frl.publisso.de/adhoc/creator/THXFoXRyZWssIE1pdGph|https://frl.publisso.de/adhoc/creator/U2NobWlkdCwgUmFpbmVy|https://frl.publisso.de/adhoc/creator/UmVwc2lsYmVyLCBEaXJr|https://frl.publisso.de/adhoc/creator/RnVlbGxlbiwgR2Vvcmc=
1000 Förderer
  1. Deutsche Forschungsgemeinschaft (DFG) |
1000 Fördernummer
  1. SPP 1356; FU583/2-1; FU583/2-2
1000 Förderprogramm
  1. Pluripotency and Cellular Reprogramming
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Deutsche Forschungsgemeinschaft (DFG) |
    1000 Förderprogramm Pluripotency and Cellular Reprogramming
    1000 Fördernummer SPP 1356; FU583/2-1; FU583/2-2
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6404481.rdf
1000 Erstellt am 2017-09-15T16:04:41.747+0200
1000 Erstellt von 218
1000 beschreibt frl:6404481
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Thu Sep 27 12:52:16 CEST 2018
1000 Objekt bearb. Thu Sep 27 12:52:15 CEST 2018
1000 Vgl. frl:6404481
1000 Oai Id
  1. oai:frl.publisso.de:frl:6404481 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
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