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

@inproceedings{Palermo2009PerformanceOP,
  title={Performance of PLS regression coefficients in selecting variables for each response of a multivariate PLS for omics-type data},
  author={Giuseppe Palermo and Paolo Piraino and Hans-Dieter Zucht},
  booktitle={Advances and applications in bioinformatics and chemistry : AABC},
  year={2009}
}
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… CONTINUE READING