The Handbook of Metabolic Phenotyping, Volume 33, explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales.
- Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery
- Includes comprehensible, theoretical chapters written for large and diverse audiences
- Provides a wealth of selected application to the topics included
1. Preface 2. Introduction 3. Framework for low-level data fusion 4. Numerical optimization based algorithms for data fusion 5. General framing of low-high-mid level Data Fusion with examples in life science 6. SO-(N)-PLS: Sequentially Orthogonalized-(N)-PLS in Data Fusion context 7. ComDim methods for the analysis of multi block data in a data fusion perspective 8. Data fusion via multiset analysis 9. Recent advances in High-Level Fusion Methods to classify multiple analytical Chemical Data 10. Data Fusion strategies in food analysis 11. Data fusion from a data management perspective: models, methodologies, and algorithms 12. Conceptual discussion of fusion: benefits, drawbacks, uniqueness, robustness, new possibilities for analysis 13. Data fusion for image analysis 14. Data fusion in process monitoring context 15. GSVD based approaches to combine genomic data in biomedicine 16. Regularized Generalized Canonical Correlation Analysis in the analysis of multimodal data with application to medical imaging