General Framing of Low-, Mid-, and High-Level Data Fusion With Examples in the Life Sciences

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.