journal.pone.0076561.PDF 880,50KB
1000 Titel
  • Identifying Genes Relevant to Specific Biological Conditions in Time Course Microarray Experiments
1000 Autor/in
  1. Singh, Nitesh Kumar |
  2. Repsilber, Dirk |
  3. Liebscher, Volkmar |
  4. Taher, Leila |
  5. Fuellen, Georg |
1000 Erscheinungsjahr 2013
1000 LeibnizOpen
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2013-10-11
1000 Erschienen in
1000 Quellenangabe
  • 8(10):e76561
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2013
1000 Lizenz
1000 Verlagsversion
  • |
  • |
1000 Ergänzendes Material
  • |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles. Specifically, the often large number of non-informative genes on the microarray adversely affects the performance and efficiency of classification algorithms. Furthermore, the skewed ratio of sample to variable poses a risk of overfitting. Thus, in this context, feature selection methods become crucial to select relevant genes and, hence, improve classification accuracy. In this study, we investigated feature selection methods based on gene expression profiles and protein interactions. We found that in our setup, the addition of protein interaction information did not contribute to any significant improvement of the classification results. Furthermore, we developed a novel feature selection method that relies exclusively on observed gene expression changes in microarray experiments, which we call “relative Signal-to-Noise ratio” (rSNR). More precisely, the rSNR ranks genes based on their specificity to an experimental condition, by comparing intrinsic variation, i.e. variation in gene expression within an experimental condition, with extrinsic variation, i.e. variation in gene expression across experimental conditions. Genes with low variation within an experimental condition of interest and high variation across experimental conditions are ranked higher, and help in improving classification accuracy. We compared different feature selection methods on two time-series microarray datasets and one static microarray dataset. We found that the rSNR performed generally better than the other methods.
1000 Sacherschließung
lokal Microarrays
lokal Toxins
lokal Database searching
lokal Gene expression
lokal Protein interactions
lokal Data reduction
lokal Linkage analysis
lokal Signal to noise ratio
1000 Fachgruppe
  1. Biologie |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
1000 (Academic) Editor
1000 Förderer
  1. German Research Foundation (DFG) |
1000 Fördernummer
  1. FUE 583/2-2
1000 Förderprogramm
  1. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer German Research Foundation (DFG) |
    1000 Förderprogramm -
    1000 Fördernummer FUE 583/2-2
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6410927.rdf
1000 Erstellt am 2018-11-06T14:46:11.337+0100
1000 Erstellt von 25
1000 beschreibt frl:6410927
1000 Bearbeitet von 25
1000 Zuletzt bearbeitet Tue Nov 06 15:00:24 CET 2018
1000 Objekt bearb. Tue Nov 06 15:00:23 CET 2018
1000 Vgl. frl:6410927
1000 Oai Id
  1. |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

View source