A weighted density-based approach for identifying standardized items that are significantly related to the biological literature

A large part of scientific knowledge is confined to the text of publications. An algorithm is presented for distinguishing those pieces of information that can be predicted from the text of publication abstracts from those, for successes in prediction are spurious. The significance of relationships between textual data and information that is represented in standardized ontologies and protein domains is evaluated using a density-based approach. The approach also integrates a weighting system to account for many-to-many relationships between the abstracts and the genes they represent as well as between genes and the items that describe them. We evaluate the approach using data related from the model species yeast, and show that our results are in better agreement with biological expectations than a comparison algorithm.