Information Fusion (IF) is about combining, or fusing, information from different sources in order to facilitate our understanding of a complex system and thereby provide insights that could not be gained from any of the individual data sources in isolation. We argue in this paper that there is a need for applying an IF approach in bioinformatics research, since the aim of bioinformatics is to understand complex biological systems using many different data sources providing complementary views of the system. We illustrate this argument with two application examples, where IF-based bioinformatics is applied to the study of stem cell differentiation and lipid digestion, respectively. We also discuss the use of automated information extraction from text sources, which is an essential component of a bioinformatics IF approach, given the abundant literature.