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Key Features
- 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
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About the Book
Data Fusion Methodology and Applications 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.
Readership
Graduate students and researchers in chemical, biochemical, and biomedical disciplines where multi-analytical platforms are most diffuse/used (hyphenated instruments, imaging spectroscopies, microarrays, sensors, bio-sensors, etc.) and whose research areas include life science (systems biology, genomics, proteomics, metabolomics), food science (authentication, adulteration, sensory analysis, nutraceuticals), and industrial process monitoring
Content
View Table of Contents
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