  In systems biology, it is common to measure biochemical entities at different
levels of the same biological system. One of the central problems for the data
fusion of such data sets is the heterogeneity of the data. This thesis
discusses two types of heterogeneity. The first one is the type of data, such
as metabolomics, proteomics and RNAseq data in genomics. These different omics
data reflect the properties of the studied biological system from different
perspectives. The second one is the type of scale, which indicates the
measurements obtained at different scales, such as binary, ordinal, interval
and ratio-scaled variables. In this thesis, we developed several statistical
methods capable to fuse data sets of these two types of heterogeneity. The
advantages of the proposed methods in comparison with other approaches are
assessed using comprehensive simulations as well as the analysis of real
biological data sets.
