BACKGROUND: Biological knowledge is represented in scientific literature that 
often describes the function of genes/proteins (bioentities) in terms of their 
interactions (biointeractions). Such bioentities are often related to biological 
concepts of interest that are specific of a determined research field. 
Therefore, the study of the current literature about a selected topic deposited 
in public databases, facilitates the generation of novel hypotheses associating 
a set of bioentities to a common context.
RESULTS: We created a text mining system (LAITOR: Literature Assistant for 
Identification of Terms co-Occurrences and Relationships) that analyses 
co-occurrences of bioentities, biointeractions, and other biological terms in 
MEDLINE abstracts. The method accounts for the position of the co-occurring 
terms within sentences or abstracts. The system detected abstracts mentioning 
protein-protein interactions in a standard test (BioCreative II IAS test data) 
with a precision of 0.82-0.89 and a recall of 0.48-0.70. We illustrate the 
application of LAITOR to the detection of plant response genes in a dataset of 
1000 abstracts relevant to the topic.
CONCLUSIONS: Text mining tools combining the extraction of interacting 
bioentities and biological concepts with network displays can be helpful in 
developing reasonable hypotheses in different scientific backgrounds.