Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data.

ABSTRACT: Multivariate partial least square (PLS) regression allows the modeling of complex biological events, by considering different factors at the same time. It is unaffected by data collinearity, representing a valuable method for modeling high-dimensional biological data (as derived from genomics, proteomics and peptidomics). In presence of multiple responses, it is of particular interest how to appropriately "dissect" the model, to reveal the importance of single attributes with regard to individual responses (for example, variable selection). In this paper, performances of multivariate PLS regression coefficients, in selecting relevant predictors for different responses in omics-type of data, were investigated by means of a receiver operating characteristic (ROC) analysis. For this purpose,

SUBMITTER: Palermo G 

PROVIDER: S-EPMC3169946 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature