In order to understand how biological systems function it is necessary to 
determine the interactions and associations between proteins. Some proteins, 
involved in a common biological process and encoded by separate genes in one 
organism, can be found fused within a single protein chain in other organisms. 
By detecting these triplets, a functional relationship can be established 
between the unfused proteins. Here we use a domain fusion prediction method to 
predict these protein interactions for the human interactome. We observed that 
gene fusion events are more related to physical interaction between proteins 
than to other weaker functional relationships such as participation in a common 
biological pathway. These results suggest that domain fusion is an appropriate 
method for predicting protein complexes. The most reliable fused domain 
predictions were used to build protein-protein interaction (PPI) networks. These 
predicted PPI network models showed the same topological features as real 
biological networks and different features from random behaviour. We built the 
PPI domain fusion sub-network model of the human kinetochore and observed that 
the majority of the predicted interactions have not yet been experimentally 
characterised in the publicly available PPI repositories. The study of the human 
kinetochore domain fusion sub-network reveals undiscovered kinetochore proteins 
with presumably relevant functions, such as hubs with many connections in the 
kinetochore sub-network. These results suggest that experimentally hidden 
regions in the predicted PPI networks contain key functional elements, 
associated with important functional areas, still undiscovered in the human 
interactome. Until novel experiments shed light on these hidden regions; domain 
fusion predictions provide a valuable approach for exploring them.