Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 550905
Title General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences
Author(s) Smolinska, Agnieszka; Engel, Jasper; Szymanska, Ewa; Buydens, Lutgarde; Blanchet, Lionel
Source In: Data Fusion Methodology and Applications Elsevier Ltd, Academic Press (Data Handling in Science and Technology ) - ISBN 9780444639844 - p. 51 - 79.
Department(s) Biometris
Publication type Peer reviewed book chapter
Publication year 2019
Keyword(s) Analytical technique - Data fusion - Gas chromatography–mass spectrometry - Kernel-based data fusion - Liquid chromatography - Microbiome data

The constant development of analytical techniques leads to an increase in the amount of information available to describe phenomena in life science. In parallel, the inherent complexity of life science makes it almost impossible to obtain a comprehensive description using only one technical modality. Therefore, it became very popular to combine several biological or technical platforms/modalities to obtain a better understanding of the underlying problems. Merging different types of measurements/platforms into a single analysis is, however, a complex topic. Combining various platforms into single analysis is defined as data fusion. We describe here different types of data fusion strategies: the well-established low-, mid-, and high-level data fusion and the more recently introduced sustainable mid-level data fusion and kernel-based data fusion. For each type, we provide a detailed description. To illustrate these various data fusion approaches, we rely on four real data sets, namely, exhaled breath data of patients with Crohn disease (CD) obtained by gas chromatography–mass spectrometry (GC-MS), 454 pyrosequencing microbiome data of patients with CD, and metabolic profiling of beer brands by GC-MS and positive and negative ion modes of liquid chromatography.

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