|
Résumé :
|
This second volume has two parts, the first with specialized applications of multi- and mega-variate analysis, namely: QSAR (quantitative structure-activity relationships) describes how series of molecular structures can be translated to quantitative data and how these data then are used to model and predict biological activity measurements made on the corresponding molecules. Chapters on how the QSAR concept applies in peptide QSAR, lead finding and optimization, combinatorial chemistry, and chem-and bio-informatics, are included. The multi- and megavariate analysis of "omics" data, has a special chapter, i.e., data from metabonomics, proteomics, genomics and other areas. Then follow six chapters on extensions of the basic projection methods (PCA and PLS): Orthogonal PLS (OPLS) showing how a PLS model can be "rotated" so that all y-related information appears in the first component, which facilitates the model interpretation. Hierarchical modeling, both PC and PLS, allowing variables of different types to be handled in separate blocks, which greatly simplifies the handling of datasets with very many variables. Non-linear PLS describes various approaches to the modeling of non-linear relationships between predictors X and responses Y. The Image Analysis chapter shows how multivariate analysis applies to the analysis of series of digital images. Data Mining and Integration has a discussion of how to get useful information out of large and complicated data sets, and how to manage and organize data in complex investigations. The second volume ends with a chapter on preference and sensory data, followed by an appendix summarizing the multivariate approach, statistical notes, and references.
|
