Motivation: The promises of the post-genome era disease-related discoveries and advances have yet to be fully realized, with many opportunities for discovery hiding in the millions of biomedical papers published since. Public databases give access to data extracted from the literature by teams of experts, but their coverage is often limited and lags behind recent discoveries. We present a computational method that combines data extracted from the literature with data from curated sources in order to uncover possible gene-disease relationships that are not directly stated or were missed by the initial mining. Method: An initial set of genes and proteins is obtained from gene-disease relationships extracted from PubMed abstracts using natural language processing. Interactions involving the corresponding proteins are similarly extracted and integrated with interactions from curated databases (such as BIND and DIP), assigning a confidence measure to each interaction depending on its source. The augmented list of genes and gene products is then ranked combining two scores: one that reflects the strength of the relationship with the initial set of genes and incorporates user-defined weights and another that reflects the importance of the gene in maintaining the connectivity of the network. We applied the method to atherosclerosis to assess its effectiveness. Results: Top-ranked proteins from the method are related to atherosclerosis with accuracy between 0.85 to 1.00 for the top 20 and 0.64 to 0.80 for the top 90 if duplicates are ignored, with 45% of the top 20 and 75% of the top 90 derived by the method, not extracted from text. Thus, though the initial gene set and interactions were automatically extracted from text (and subject to the impreciseness of automatic extraction), their use for further hypothesis generation is valuable given adequate computational analysis.