  In systems biology, it is becoming increasingly common to measure biochemical
entities at different levels of the same biological system. Hence, data fusion
problems are abundant in the life sciences. With the availability of a
multitude of measuring techniques, one of the central problems is the
heterogeneity of the data. In this paper, we discuss a specific form of
heterogeneity, namely that of measurements obtained at different measurement
scales, such as binary, ordinal, interval and ratio-scaled variables. Three
generic fusion approaches are presented of which two are new to the systems
biology community. The methods are presented, put in context and illustrated
with a real-life genomics example.
