Download
journal.pone.0055267.PDF 943,24KB
WeightNameValue
1000 Titel
  • An Extension of PPLS-DA for Classification and Comparison to Ordinary PLS-DA
1000 Autor/in
  1. Telaar, Anna |
  2. Liland, Kristian Hovde |
  3. Repsilber, Dirk |
  4. Nürnberg, Gerd |
1000 Erscheinungsjahr 2013
1000 LeibnizOpen
1000 Art der Datei
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2013-02-11
1000 Erschienen in
1000 Quellenangabe
  • 8(2):e55267
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2013
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1371/journal.pone.0055267 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3569448/ |
1000 Ergänzendes Material
  • https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0055267#s5 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Classification studies are widely applied, e.g. in biomedical research to classify objects/patients into predefined groups. The goal is to find a classification function/rule which assigns each object/patient to a unique group with the greatest possible accuracy (classification error). Especially in gene expression experiments often a lot of variables (genes) are measured for only few objects/patients. A suitable approach is the well-known method PLS-DA, which searches for a transformation to a lower dimensional space. Resulting new components are linear combinations of the original variables. An advancement of PLS-DA leads to PPLS-DA, introducing a so called ‘power parameter’, which is maximized towards the correlation between the components and the group-membership. We introduce an extension of PPLS-DA for optimizing this power parameter towards the final aim, namely towards a minimal classification error. We compare this new extension with the original PPLS-DA and also with the ordinary PLS-DA using simulated and experimental datasets. For the investigated data sets with weak linear dependency between features/variables, no improvement is shown for PPLS-DA and for the extensions compared to PLS-DA. A very weak linear dependency, a low proportion of differentially expressed genes for simulated data, does not lead to an improvement of PPLS-DA over PLS-DA, but our extension shows a lower prediction error. On the contrary, for the data set with strong between-feature collinearity and a low proportion of differentially expressed genes and a large total number of genes, the prediction error of PPLS-DA and the extensions is clearly lower than for PLS-DA. Moreover we compare these prediction results with results of support vector machines with linear kernel and linear discriminant analysis.
1000 Sacherschließung
lokal Prostate gland
lokal Covariance
lokal Eigenvalues
lokal Breast cancer
lokal Lymphomas
lokal Gene expression
lokal Simulation and modeling
lokal Leukemias
1000 Fachgruppe
  1. Biologie |
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/creator/VGVsYWFyLCBBbm5h|https://frl.publisso.de/adhoc/creator/TGlsYW5kLCBLcmlzdGlhbiBIb3ZkZQ==|https://frl.publisso.de/adhoc/creator/UmVwc2lsYmVyLCBEaXJr|http://orcid.org/0000-0002-4968-5675
1000 (Academic) Editor
1000 Förderer
  1. Max Planck Institute for Infection Biology, Berlin |
  2. German Federal Minstry of Education and Research |
1000 Fördernummer
  1. -
  2. -
1000 Förderprogramm
  1. -
  2. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Max Planck Institute for Infection Biology, Berlin |
    1000 Förderprogramm -
    1000 Fördernummer -
  2. 1000 joinedFunding-child
    1000 Förderer German Federal Minstry of Education and Research |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6410929.rdf
1000 Erstellt am 2018-11-06T14:55:20.132+0100
1000 Erstellt von 25
1000 beschreibt frl:6410929
1000 Bearbeitet von 218
1000 Zuletzt bearbeitet Tue Dec 18 12:23:58 CET 2018
1000 Objekt bearb. Tue Dec 18 12:23:58 CET 2018
1000 Vgl. frl:6410929
1000 Oai Id
  1. oai:frl.publisso.de:frl:6410929 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

View source